Overview

Dataset statistics

Number of variables144
Number of observations69948
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.9 MiB
Average record size in memory403.0 B

Variable types

Numeric11
Categorical133

Alerts

insulin is highly overall correlated with change and 1 other fieldsHigh correlation
change is highly overall correlated with insulin and 2 other fieldsHigh correlation
diabetesMed is highly overall correlated with insulin and 1 other fieldsHigh correlation
numchange is highly overall correlated with changeHigh correlation
max_glu_serum is highly imbalanced (79.5%)Imbalance
repaglinide is highly imbalanced (90.0%)Imbalance
nateglinide is highly imbalanced (94.0%)Imbalance
chlorpropamide is highly imbalanced (98.9%)Imbalance
glimepiride is highly imbalanced (70.2%)Imbalance
acetohexamide is highly imbalanced (> 99.9%)Imbalance
glyburide is highly imbalanced (50.1%)Imbalance
tolbutamide is highly imbalanced (99.7%)Imbalance
pioglitazone is highly imbalanced (61.6%)Imbalance
rosiglitazone is highly imbalanced (65.0%)Imbalance
acarbose is highly imbalanced (97.1%)Imbalance
miglitol is highly imbalanced (99.6%)Imbalance
troglitazone is highly imbalanced (> 99.9%)Imbalance
tolazamide is highly imbalanced (99.5%)Imbalance
glyburide-metformin is highly imbalanced (94.0%)Imbalance
glipizide-metformin is highly imbalanced (99.9%)Imbalance
metformin-rosiglitazone is highly imbalanced (> 99.9%)Imbalance
metformin-pioglitazone is highly imbalanced (> 99.9%)Imbalance
numchange is highly imbalanced (61.4%)Imbalance
race_Asian is highly imbalanced (94.0%)Imbalance
race_Hispanic is highly imbalanced (85.1%)Imbalance
race_Other is highly imbalanced (87.9%)Imbalance
age_[10-20) is highly imbalanced (93.6%)Imbalance
age_[20-30) is highly imbalanced (88.2%)Imbalance
age_[30-40) is highly imbalanced (76.5%)Imbalance
age_[40-50) is highly imbalanced (54.1%)Imbalance
age_[90-100) is highly imbalanced (82.7%)Imbalance
medical_specialty_Anesthesiology is highly imbalanced (99.9%)Imbalance
medical_specialty_Anesthesiology-Pediatric is highly imbalanced (99.7%)Imbalance
medical_specialty_Cardiology is highly imbalanced (67.5%)Imbalance
medical_specialty_Cardiology-Pediatric is highly imbalanced (99.9%)Imbalance
medical_specialty_DCPTEAM is highly imbalanced (99.9%)Imbalance
medical_specialty_Dentistry is highly imbalanced (99.9%)Imbalance
medical_specialty_Dermatology is highly imbalanced (> 99.9%)Imbalance
medical_specialty_Emergency/Trauma is highly imbalanced (66.2%)Imbalance
medical_specialty_Endocrinology is highly imbalanced (98.5%)Imbalance
medical_specialty_Endocrinology-Metabolism is highly imbalanced (99.9%)Imbalance
medical_specialty_Family/GeneralPractice is highly imbalanced (63.0%)Imbalance
medical_specialty_Gastroenterology is highly imbalanced (95.1%)Imbalance
medical_specialty_Gynecology is highly imbalanced (99.1%)Imbalance
medical_specialty_Hematology is highly imbalanced (99.3%)Imbalance
medical_specialty_Hematology/Oncology is highly imbalanced (98.3%)Imbalance
medical_specialty_Hospitalist is highly imbalanced (99.4%)Imbalance
medical_specialty_InfectiousDiseases is highly imbalanced (99.4%)Imbalance
medical_specialty_Nephrology is highly imbalanced (90.8%)Imbalance
medical_specialty_Neurology is highly imbalanced (97.6%)Imbalance
medical_specialty_Neurophysiology is highly imbalanced (> 99.9%)Imbalance
medical_specialty_Obsterics&Gynecology-GynecologicOnco is highly imbalanced (99.6%)Imbalance
medical_specialty_Obstetrics is highly imbalanced (99.7%)Imbalance
medical_specialty_ObstetricsandGynecology is highly imbalanced (92.9%)Imbalance
medical_specialty_Oncology is highly imbalanced (97.1%)Imbalance
medical_specialty_Ophthalmology is highly imbalanced (99.4%)Imbalance
medical_specialty_Orthopedics is highly imbalanced (88.2%)Imbalance
medical_specialty_Orthopedics-Reconstructive is highly imbalanced (88.8%)Imbalance
medical_specialty_Osteopath is highly imbalanced (99.3%)Imbalance
medical_specialty_Otolaryngology is highly imbalanced (98.4%)Imbalance
medical_specialty_OutreachServices is highly imbalanced (99.8%)Imbalance
medical_specialty_Pathology is highly imbalanced (99.9%)Imbalance
medical_specialty_Pediatrics is highly imbalanced (97.3%)Imbalance
medical_specialty_Pediatrics-AllergyandImmunology is highly imbalanced (> 99.9%)Imbalance
medical_specialty_Pediatrics-CriticalCare is highly imbalanced (98.8%)Imbalance
medical_specialty_Pediatrics-EmergencyMedicine is highly imbalanced (99.9%)Imbalance
medical_specialty_Pediatrics-Endocrinology is highly imbalanced (97.9%)Imbalance
medical_specialty_Pediatrics-Hematology-Oncology is highly imbalanced (99.9%)Imbalance
medical_specialty_Pediatrics-Neurology is highly imbalanced (99.9%)Imbalance
medical_specialty_Pediatrics-Pulmonology is highly imbalanced (99.9%)Imbalance
medical_specialty_Perinatology is highly imbalanced (> 99.9%)Imbalance
medical_specialty_PhysicalMedicineandRehabilitation is highly imbalanced (97.3%)Imbalance
medical_specialty_PhysicianNotFound is highly imbalanced (99.9%)Imbalance
medical_specialty_Podiatry is highly imbalanced (99.0%)Imbalance
medical_specialty_Proctology is highly imbalanced (> 99.9%)Imbalance
medical_specialty_Psychiatry is highly imbalanced (92.8%)Imbalance
medical_specialty_Psychiatry-Addictive is highly imbalanced (> 99.9%)Imbalance
medical_specialty_Psychiatry-Child/Adolescent is highly imbalanced (99.9%)Imbalance
medical_specialty_Psychology is highly imbalanced (99.1%)Imbalance
medical_specialty_Pulmonology is highly imbalanced (92.6%)Imbalance
medical_specialty_Radiologist is highly imbalanced (90.8%)Imbalance
medical_specialty_Radiology is highly imbalanced (99.4%)Imbalance
medical_specialty_Resident is highly imbalanced (> 99.9%)Imbalance
medical_specialty_Rheumatology is highly imbalanced (99.8%)Imbalance
medical_specialty_Speech is highly imbalanced (> 99.9%)Imbalance
medical_specialty_Surgeon is highly imbalanced (99.3%)Imbalance
medical_specialty_Surgery-Cardiovascular is highly imbalanced (98.6%)Imbalance
medical_specialty_Surgery-Cardiovascular/Thoracic is highly imbalanced (94.0%)Imbalance
medical_specialty_Surgery-Colon&Rectal is highly imbalanced (99.8%)Imbalance
medical_specialty_Surgery-General is highly imbalanced (79.7%)Imbalance
medical_specialty_Surgery-Maxillofacial is highly imbalanced (99.9%)Imbalance
medical_specialty_Surgery-Neuro is highly imbalanced (94.8%)Imbalance
medical_specialty_Surgery-Pediatric is highly imbalanced (99.9%)Imbalance
medical_specialty_Surgery-Plastic is highly imbalanced (99.5%)Imbalance
medical_specialty_Surgery-PlasticwithinHeadandNeck is highly imbalanced (> 99.9%)Imbalance
medical_specialty_Surgery-Thoracic is highly imbalanced (98.5%)Imbalance
medical_specialty_Surgery-Vascular is highly imbalanced (95.3%)Imbalance
medical_specialty_SurgicalSpecialty is highly imbalanced (99.5%)Imbalance
medical_specialty_Urology is highly imbalanced (93.6%)Imbalance
diag_1_Diabetes is highly imbalanced (59.4%)Imbalance
diag_1_Digestive is highly imbalanced (55.3%)Imbalance
diag_1_Genitourinary is highly imbalanced (71.5%)Imbalance
diag_1_Injury is highly imbalanced (64.3%)Imbalance
diag_1_Muscoloskeletal is highly imbalanced (68.2%)Imbalance
diag_1_Others is highly imbalanced (71.4%)Imbalance
diag_2_Digestive is highly imbalanced (75.4%)Imbalance
diag_2_Genitourinary is highly imbalanced (60.7%)Imbalance
diag_2_Injury is highly imbalanced (82.2%)Imbalance
diag_2_Muscoloskeletal is highly imbalanced (86.5%)Imbalance
diag_2_Others is highly imbalanced (60.5%)Imbalance
diag_2_Respiratory is highly imbalanced (53.2%)Imbalance
diag_3_Digestive is highly imbalanced (76.4%)Imbalance
diag_3_Genitourinary is highly imbalanced (67.4%)Imbalance
diag_3_Injury is highly imbalanced (85.4%)Imbalance
diag_3_Muscoloskeletal is highly imbalanced (85.9%)Imbalance
diag_3_Others is highly imbalanced (52.7%)Imbalance
diag_3_Respiratory is highly imbalanced (64.3%)Imbalance
number_emergency is highly skewed (γ1 = 29.93624225)Skewed
num_procedures has 30766 (44.0%) zerosZeros
number_outpatient has 60361 (86.3%) zerosZeros
number_emergency has 64389 (92.1%) zerosZeros
number_inpatient has 58403 (83.5%) zerosZeros

Reproduction

Analysis started2023-06-05 04:03:54.947729
Analysis finished2023-06-05 04:07:07.656119
Duration3 minutes and 12.71 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

admission_type_id
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0918825
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.6 KiB
2023-06-04T23:07:07.746116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4975567
Coefficient of variation (CV)0.7158895
Kurtosis1.7424084
Mean2.0918825
Median Absolute Deviation (MAD)0
Skewness1.5404884
Sum146323
Variance2.2426762
MonotonicityNot monotonic
2023-06-04T23:07:07.948555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 35689
51.0%
3 13804
 
19.7%
2 12782
 
18.3%
6 4365
 
6.2%
5 2981
 
4.3%
8 301
 
0.4%
7 17
 
< 0.1%
4 9
 
< 0.1%
ValueCountFrequency (%)
1 35689
51.0%
2 12782
 
18.3%
3 13804
 
19.7%
4 9
 
< 0.1%
5 2981
 
4.3%
6 4365
 
6.2%
7 17
 
< 0.1%
8 301
 
0.4%
ValueCountFrequency (%)
8 301
 
0.4%
7 17
 
< 0.1%
6 4365
 
6.2%
5 2981
 
4.3%
4 9
 
< 0.1%
3 13804
 
19.7%
2 12782
 
18.3%
1 35689
51.0%

discharge_disposition_id
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4102333
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.6 KiB
2023-06-04T23:07:08.137675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.1419906
Coefficient of variation (CV)1.507812
Kurtosis7.5240002
Mean3.4102333
Median Absolute Deviation (MAD)0
Skewness2.85215
Sum238539
Variance26.440067
MonotonicityNot monotonic
2023-06-04T23:07:08.330772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 44028
62.9%
3 8990
 
12.9%
6 8403
 
12.0%
18 2489
 
3.6%
2 1522
 
2.2%
22 1453
 
2.1%
5 907
 
1.3%
25 672
 
1.0%
4 537
 
0.8%
7 421
 
0.6%
Other values (11) 526
 
0.8%
ValueCountFrequency (%)
1 44028
62.9%
2 1522
 
2.2%
3 8990
 
12.9%
4 537
 
0.8%
5 907
 
1.3%
6 8403
 
12.0%
7 421
 
0.6%
8 72
 
0.1%
9 12
 
< 0.1%
10 6
 
< 0.1%
ValueCountFrequency (%)
28 99
 
0.1%
27 3
 
< 0.1%
25 672
 
1.0%
24 26
 
< 0.1%
23 254
 
0.4%
22 1453
2.1%
18 2489
3.6%
17 6
 
< 0.1%
16 2
 
< 0.1%
15 44
 
0.1%

admission_source_id
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6438211
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.6 KiB
2023-06-04T23:07:08.770401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1500275
Coefficient of variation (CV)0.73532229
Kurtosis1.6596486
Mean5.6438211
Median Absolute Deviation (MAD)0
Skewness1.0701371
Sum394774
Variance17.222728
MonotonicityNot monotonic
2023-06-04T23:07:08.960759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7 37424
53.5%
1 21577
30.8%
17 4787
 
6.8%
4 2526
 
3.6%
6 1790
 
2.6%
2 904
 
1.3%
5 540
 
0.8%
20 144
 
0.2%
3 130
 
0.2%
9 98
 
0.1%
Other values (7) 28
 
< 0.1%
ValueCountFrequency (%)
1 21577
30.8%
2 904
 
1.3%
3 130
 
0.2%
4 2526
 
3.6%
5 540
 
0.8%
6 1790
 
2.6%
7 37424
53.5%
8 12
 
< 0.1%
9 98
 
0.1%
10 6
 
< 0.1%
ValueCountFrequency (%)
25 2
 
< 0.1%
22 4
 
< 0.1%
20 144
 
0.2%
17 4787
6.8%
14 2
 
< 0.1%
13 1
 
< 0.1%
11 1
 
< 0.1%
10 6
 
< 0.1%
9 98
 
0.1%
8 12
 
< 0.1%

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2755618
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.6 KiB
2023-06-04T23:07:09.135982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9351421
Coefficient of variation (CV)0.68649273
Kurtosis1.0281505
Mean4.2755618
Median Absolute Deviation (MAD)2
Skewness1.1835209
Sum299067
Variance8.6150592
MonotonicityNot monotonic
2023-06-04T23:07:09.318853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 12532
17.9%
2 12146
17.4%
1 10425
14.9%
4 9456
13.5%
5 6680
9.5%
6 5058
7.2%
7 3888
 
5.6%
8 2847
 
4.1%
9 1902
 
2.7%
10 1511
 
2.2%
Other values (4) 3503
 
5.0%
ValueCountFrequency (%)
1 10425
14.9%
2 12146
17.4%
3 12532
17.9%
4 9456
13.5%
5 6680
9.5%
6 5058
7.2%
7 3888
 
5.6%
8 2847
 
4.1%
9 1902
 
2.7%
10 1511
 
2.2%
ValueCountFrequency (%)
14 657
 
0.9%
13 771
 
1.1%
12 916
 
1.3%
11 1159
 
1.7%
10 1511
 
2.2%
9 1902
 
2.7%
8 2847
4.1%
7 3888
5.6%
6 5058
7.2%
5 6680
9.5%

num_lab_procedures
Real number (ℝ)

Distinct116
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.028364
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.6 KiB
2023-06-04T23:07:09.538924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.815893
Coefficient of variation (CV)0.46053094
Kurtosis-0.28609917
Mean43.028364
Median Absolute Deviation (MAD)13
Skewness-0.22700744
Sum3009748
Variance392.66962
MonotonicityNot monotonic
2023-06-04T23:07:09.779215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2200
 
3.1%
43 1881
 
2.7%
44 1646
 
2.4%
45 1599
 
2.3%
46 1520
 
2.2%
38 1514
 
2.2%
40 1496
 
2.1%
47 1474
 
2.1%
41 1447
 
2.1%
37 1414
 
2.0%
Other values (106) 53757
76.9%
ValueCountFrequency (%)
1 2200
3.1%
2 755
 
1.1%
3 485
 
0.7%
4 283
 
0.4%
5 208
 
0.3%
6 194
 
0.3%
7 254
 
0.4%
8 256
 
0.4%
9 663
 
0.9%
10 573
 
0.8%
ValueCountFrequency (%)
132 1
 
< 0.1%
121 1
 
< 0.1%
120 1
 
< 0.1%
118 1
 
< 0.1%
114 1
 
< 0.1%
113 2
< 0.1%
111 2
< 0.1%
109 1
 
< 0.1%
108 3
< 0.1%
107 1
 
< 0.1%

num_procedures
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4217419
Minimum0
Maximum6
Zeros30766
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size546.6 KiB
2023-06-04T23:07:09.970865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7519348
Coefficient of variation (CV)1.2322453
Kurtosis0.58063744
Mean1.4217419
Median Absolute Deviation (MAD)1
Skewness1.2324284
Sum99448
Variance3.0692755
MonotonicityNot monotonic
2023-06-04T23:07:10.125550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 30766
44.0%
1 14105
20.2%
2 8978
 
12.8%
3 6971
 
10.0%
6 3807
 
5.4%
4 2973
 
4.3%
5 2348
 
3.4%
ValueCountFrequency (%)
0 30766
44.0%
1 14105
20.2%
2 8978
 
12.8%
3 6971
 
10.0%
4 2973
 
4.3%
5 2348
 
3.4%
6 3807
 
5.4%
ValueCountFrequency (%)
6 3807
 
5.4%
5 2348
 
3.4%
4 2973
 
4.3%
3 6971
 
10.0%
2 8978
 
12.8%
1 14105
20.2%
0 30766
44.0%

num_medications
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.743052
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.6 KiB
2023-06-04T23:07:10.340730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median14
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.287482
Coefficient of variation (CV)0.52642156
Kurtosis3.7799376
Mean15.743052
Median Absolute Deviation (MAD)5
Skewness1.4173492
Sum1101195
Variance68.682358
MonotonicityNot monotonic
2023-06-04T23:07:10.578249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 4218
 
6.0%
12 4161
 
5.9%
11 4003
 
5.7%
15 3928
 
5.6%
14 3862
 
5.5%
10 3801
 
5.4%
16 3630
 
5.2%
9 3555
 
5.1%
17 3323
 
4.8%
8 3177
 
4.5%
Other values (65) 32290
46.2%
ValueCountFrequency (%)
1 213
 
0.3%
2 384
 
0.5%
3 701
 
1.0%
4 1123
 
1.6%
5 1560
2.2%
6 2063
2.9%
7 2611
3.7%
8 3177
4.5%
9 3555
5.1%
10 3801
5.4%
ValueCountFrequency (%)
81 1
 
< 0.1%
79 1
 
< 0.1%
75 2
 
< 0.1%
74 1
 
< 0.1%
72 2
 
< 0.1%
70 1
 
< 0.1%
69 4
< 0.1%
68 4
< 0.1%
67 5
< 0.1%
66 4
< 0.1%

number_outpatient
Real number (ℝ)

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29895065
Minimum0
Maximum40
Zeros60361
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size546.6 KiB
2023-06-04T23:07:10.814678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1144728
Coefficient of variation (CV)3.727949
Kurtosis176.47397
Mean0.29895065
Median Absolute Deviation (MAD)0
Skewness9.5632283
Sum20911
Variance1.2420496
MonotonicityNot monotonic
2023-06-04T23:07:11.028412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 60361
86.3%
1 4950
 
7.1%
2 2091
 
3.0%
3 1161
 
1.7%
4 623
 
0.9%
5 298
 
0.4%
6 151
 
0.2%
7 84
 
0.1%
8 58
 
0.1%
9 36
 
0.1%
Other values (25) 135
 
0.2%
ValueCountFrequency (%)
0 60361
86.3%
1 4950
 
7.1%
2 2091
 
3.0%
3 1161
 
1.7%
4 623
 
0.9%
5 298
 
0.4%
6 151
 
0.2%
7 84
 
0.1%
8 58
 
0.1%
9 36
 
0.1%
ValueCountFrequency (%)
40 1
< 0.1%
37 1
< 0.1%
36 1
< 0.1%
35 1
< 0.1%
33 2
< 0.1%
29 1
< 0.1%
28 1
< 0.1%
27 1
< 0.1%
26 2
< 0.1%
25 1
< 0.1%

number_emergency
Real number (ℝ)

SKEWED  ZEROS 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11994625
Minimum0
Maximum64
Zeros64389
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size546.6 KiB
2023-06-04T23:07:11.238829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum64
Range64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.63219384
Coefficient of variation (CV)5.270643
Kurtosis2125.8574
Mean0.11994625
Median Absolute Deviation (MAD)0
Skewness29.936242
Sum8390
Variance0.39966905
MonotonicityNot monotonic
2023-06-04T23:07:11.421065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 64389
92.1%
1 4127
 
5.9%
2 886
 
1.3%
3 288
 
0.4%
4 118
 
0.2%
5 50
 
0.1%
6 35
 
0.1%
7 14
 
< 0.1%
8 10
 
< 0.1%
10 6
 
< 0.1%
Other values (13) 25
 
< 0.1%
ValueCountFrequency (%)
0 64389
92.1%
1 4127
 
5.9%
2 886
 
1.3%
3 288
 
0.4%
4 118
 
0.2%
5 50
 
0.1%
6 35
 
0.1%
7 14
 
< 0.1%
8 10
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
64 1
< 0.1%
42 1
< 0.1%
37 1
< 0.1%
28 1
< 0.1%
25 1
< 0.1%
22 1
< 0.1%
20 1
< 0.1%
19 2
< 0.1%
16 2
< 0.1%
13 2
< 0.1%

number_inpatient
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26581175
Minimum0
Maximum15
Zeros58403
Zeros (%)83.5%
Negative0
Negative (%)0.0%
Memory size546.6 KiB
2023-06-04T23:07:11.608415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.76952812
Coefficient of variation (CV)2.8950117
Kurtosis40.79568
Mean0.26581175
Median Absolute Deviation (MAD)0
Skewness5.0317276
Sum18593
Variance0.59217353
MonotonicityNot monotonic
2023-06-04T23:07:11.779516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 58403
83.5%
1 7663
 
11.0%
2 2337
 
3.3%
3 792
 
1.1%
4 368
 
0.5%
5 171
 
0.2%
6 112
 
0.2%
7 39
 
0.1%
8 26
 
< 0.1%
9 13
 
< 0.1%
Other values (5) 24
 
< 0.1%
ValueCountFrequency (%)
0 58403
83.5%
1 7663
 
11.0%
2 2337
 
3.3%
3 792
 
1.1%
4 368
 
0.5%
5 171
 
0.2%
6 112
 
0.2%
7 39
 
0.1%
8 26
 
< 0.1%
9 13
 
< 0.1%
ValueCountFrequency (%)
15 3
 
< 0.1%
14 2
 
< 0.1%
12 7
 
< 0.1%
11 6
 
< 0.1%
10 6
 
< 0.1%
9 13
 
< 0.1%
8 26
 
< 0.1%
7 39
 
0.1%
6 112
0.2%
5 171
0.2%

number_diagnoses
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2504003
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.6 KiB
2023-06-04T23:07:11.964396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9948797
Coefficient of variation (CV)0.27514063
Kurtosis-0.34827424
Mean7.2504003
Median Absolute Deviation (MAD)1
Skewness-0.74858521
Sum507151
Variance3.9795451
MonotonicityNot monotonic
2023-06-04T23:07:12.133898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 31218
44.6%
5 8762
 
12.5%
6 7470
 
10.7%
7 7378
 
10.5%
8 7345
 
10.5%
4 4351
 
6.2%
3 2307
 
3.3%
2 862
 
1.2%
1 190
 
0.3%
16 29
 
< 0.1%
Other values (6) 36
 
0.1%
ValueCountFrequency (%)
1 190
 
0.3%
2 862
 
1.2%
3 2307
 
3.3%
4 4351
 
6.2%
5 8762
 
12.5%
6 7470
 
10.7%
7 7378
 
10.5%
8 7345
 
10.5%
9 31218
44.6%
10 7
 
< 0.1%
ValueCountFrequency (%)
16 29
 
< 0.1%
15 7
 
< 0.1%
14 3
 
< 0.1%
13 10
 
< 0.1%
12 4
 
< 0.1%
11 5
 
< 0.1%
10 7
 
< 0.1%
9 31218
44.6%
8 7345
 
10.5%
7 7378
 
10.5%

max_glu_serum
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
-99
66608 
0
 
1690
1
 
1650

Length

Max length3
Median length3
Mean length2.9045005
Min length1

Characters and Unicode

Total characters203164
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row-99
3rd row-99
4th row0
5th row-99

Common Values

ValueCountFrequency (%)
-99 66608
95.2%
0 1690
 
2.4%
1 1650
 
2.4%

Length

2023-06-04T23:07:12.346340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:12.557426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
99 66608
95.2%
0 1690
 
2.4%
1 1650
 
2.4%

Most occurring characters

ValueCountFrequency (%)
9 133216
65.6%
- 66608
32.8%
0 1690
 
0.8%
1 1650
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 136556
67.2%
Dash Punctuation 66608
32.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 133216
97.6%
0 1690
 
1.2%
1 1650
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 66608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 203164
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 133216
65.6%
- 66608
32.8%
0 1690
 
0.8%
1 1650
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 133216
65.6%
- 66608
32.8%
0 1690
 
0.8%
1 1650
 
0.8%

A1Cresult
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
-99
57216 
1
8953 
0
 
3779

Length

Max length3
Median length3
Mean length2.6359581
Min length1

Characters and Unicode

Total characters184380
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row-99
3rd row-99
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-99 57216
81.8%
1 8953
 
12.8%
0 3779
 
5.4%

Length

2023-06-04T23:07:12.731903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:12.942657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
99 57216
81.8%
1 8953
 
12.8%
0 3779
 
5.4%

Most occurring characters

ValueCountFrequency (%)
9 114432
62.1%
- 57216
31.0%
1 8953
 
4.9%
0 3779
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 127164
69.0%
Dash Punctuation 57216
31.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 114432
90.0%
1 8953
 
7.0%
0 3779
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 57216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 184380
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 114432
62.1%
- 57216
31.0%
1 8953
 
4.9%
0 3779
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 184380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 114432
62.1%
- 57216
31.0%
1 8953
 
4.9%
0 3779
 
2.0%

metformin
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
55170 
1
14778 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 55170
78.9%
1 14778
 
21.1%

Length

2023-06-04T23:07:13.107203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:13.293869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 55170
78.9%
1 14778
 
21.1%

Most occurring characters

ValueCountFrequency (%)
0 55170
78.9%
1 14778
 
21.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55170
78.9%
1 14778
 
21.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55170
78.9%
1 14778
 
21.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55170
78.9%
1 14778
 
21.1%

repaglinide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69035 
1
 
913

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69035
98.7%
1 913
 
1.3%

Length

2023-06-04T23:07:13.454927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:13.659305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69035
98.7%
1 913
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 69035
98.7%
1 913
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69035
98.7%
1 913
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69035
98.7%
1 913
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69035
98.7%
1 913
 
1.3%

nateglinide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69459 
1
 
489

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69459
99.3%
1 489
 
0.7%

Length

2023-06-04T23:07:13.821210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:14.023777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69459
99.3%
1 489
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 69459
99.3%
1 489
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69459
99.3%
1 489
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69459
99.3%
1 489
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69459
99.3%
1 489
 
0.7%

chlorpropamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69881 
1
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69881
99.9%
1 67
 
0.1%

Length

2023-06-04T23:07:14.183020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:14.368356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69881
99.9%
1 67
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69881
99.9%
1 67
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69881
99.9%
1 67
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69881
99.9%
1 67
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69881
99.9%
1 67
 
0.1%

glimepiride
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
66258 
1
 
3690

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 66258
94.7%
1 3690
 
5.3%

Length

2023-06-04T23:07:14.521847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:14.704680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 66258
94.7%
1 3690
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 66258
94.7%
1 3690
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66258
94.7%
1 3690
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66258
94.7%
1 3690
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66258
94.7%
1 3690
 
5.3%

acetohexamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69947 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Length

2023-06-04T23:07:14.857796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:15.040835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

glipizide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
61036 
1
8912 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 61036
87.3%
1 8912
 
12.7%

Length

2023-06-04T23:07:15.193746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:15.378317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 61036
87.3%
1 8912
 
12.7%

Most occurring characters

ValueCountFrequency (%)
0 61036
87.3%
1 8912
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61036
87.3%
1 8912
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61036
87.3%
1 8912
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61036
87.3%
1 8912
 
12.7%

glyburide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
62272 
1
7676 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 62272
89.0%
1 7676
 
11.0%

Length

2023-06-04T23:07:15.534885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:15.718633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 62272
89.0%
1 7676
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 62272
89.0%
1 7676
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62272
89.0%
1 7676
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 62272
89.0%
1 7676
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 62272
89.0%
1 7676
 
11.0%

tolbutamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69931 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69931
> 99.9%
1 17
 
< 0.1%

Length

2023-06-04T23:07:15.873520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:16.057304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69931
> 99.9%
1 17
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69931
> 99.9%
1 17
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69931
> 99.9%
1 17
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69931
> 99.9%
1 17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69931
> 99.9%
1 17
 
< 0.1%

pioglitazone
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
64711 
1
 
5237

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 64711
92.5%
1 5237
 
7.5%

Length

2023-06-04T23:07:16.208833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:16.392992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 64711
92.5%
1 5237
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 64711
92.5%
1 5237
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64711
92.5%
1 5237
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64711
92.5%
1 5237
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64711
92.5%
1 5237
 
7.5%

rosiglitazone
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
65350 
1
 
4598

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 65350
93.4%
1 4598
 
6.6%

Length

2023-06-04T23:07:16.545269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:16.728934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 65350
93.4%
1 4598
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 65350
93.4%
1 4598
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65350
93.4%
1 4598
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65350
93.4%
1 4598
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65350
93.4%
1 4598
 
6.6%

acarbose
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69746 
1
 
202

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69746
99.7%
1 202
 
0.3%

Length

2023-06-04T23:07:16.880468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:17.064812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69746
99.7%
1 202
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 69746
99.7%
1 202
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69746
99.7%
1 202
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69746
99.7%
1 202
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69746
99.7%
1 202
 
0.3%

miglitol
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69929 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69929
> 99.9%
1 19
 
< 0.1%

Length

2023-06-04T23:07:17.216843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:17.411551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69929
> 99.9%
1 19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69929
> 99.9%
1 19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69929
> 99.9%
1 19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69929
> 99.9%
1 19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69929
> 99.9%
1 19
 
< 0.1%

troglitazone
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69946 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Length

2023-06-04T23:07:17.568908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:17.765142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

tolazamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69918 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%

Length

2023-06-04T23:07:17.918187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:18.105199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%

insulin
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
1
35922 
0
34026 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 35922
51.4%
0 34026
48.6%

Length

2023-06-04T23:07:18.256881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:18.443607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 35922
51.4%
0 34026
48.6%

Most occurring characters

ValueCountFrequency (%)
1 35922
51.4%
0 34026
48.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35922
51.4%
0 34026
48.6%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 35922
51.4%
0 34026
48.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 35922
51.4%
0 34026
48.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69464 
1
 
484

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%

Length

2023-06-04T23:07:18.602718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:18.799298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69943 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%

Length

2023-06-04T23:07:18.953390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:19.137244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69946 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Length

2023-06-04T23:07:19.291252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:19.474110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69947 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Length

2023-06-04T23:07:19.626831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:19.810843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

change
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
38374 
1
31574 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 38374
54.9%
1 31574
45.1%

Length

2023-06-04T23:07:19.965744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:20.153955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 38374
54.9%
1 31574
45.1%

Most occurring characters

ValueCountFrequency (%)
0 38374
54.9%
1 31574
45.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38374
54.9%
1 31574
45.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38374
54.9%
1 31574
45.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38374
54.9%
1 31574
45.1%

diabetesMed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
1
53287 
0
16661 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 53287
76.2%
0 16661
 
23.8%

Length

2023-06-04T23:07:20.313837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:20.501517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 53287
76.2%
0 16661
 
23.8%

Most occurring characters

ValueCountFrequency (%)
1 53287
76.2%
0 16661
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 53287
76.2%
0 16661
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 53287
76.2%
0 16661
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 53287
76.2%
0 16661
 
23.8%

readmitted
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
61153 
1
8795 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 61153
87.4%
1 8795
 
12.6%

Length

2023-06-04T23:07:20.658722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:20.843962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 61153
87.4%
1 8795
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0 61153
87.4%
1 8795
 
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61153
87.4%
1 8795
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61153
87.4%
1 8795
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61153
87.4%
1 8795
 
12.6%

numchange
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
52491 
1
16449 
2
 
929
3
 
76
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 52491
75.0%
1 16449
 
23.5%
2 929
 
1.3%
3 76
 
0.1%
4 3
 
< 0.1%

Length

2023-06-04T23:07:21.311366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:21.513057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 52491
75.0%
1 16449
 
23.5%
2 929
 
1.3%
3 76
 
0.1%
4 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 52491
75.0%
1 16449
 
23.5%
2 929
 
1.3%
3 76
 
0.1%
4 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52491
75.0%
1 16449
 
23.5%
2 929
 
1.3%
3 76
 
0.1%
4 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52491
75.0%
1 16449
 
23.5%
2 929
 
1.3%
3 76
 
0.1%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52491
75.0%
1 16449
 
23.5%
2 929
 
1.3%
3 76
 
0.1%
4 3
 
< 0.1%

race_Asian
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69458 
1
 
490

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69458
99.3%
1 490
 
0.7%

Length

2023-06-04T23:07:21.693615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:21.881222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69458
99.3%
1 490
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 69458
99.3%
1 490
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69458
99.3%
1 490
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69458
99.3%
1 490
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69458
99.3%
1 490
 
0.7%

race_Caucasian
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
1
54199 
0
15749 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 54199
77.5%
0 15749
 
22.5%

Length

2023-06-04T23:07:22.034855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:22.219850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 54199
77.5%
0 15749
 
22.5%

Most occurring characters

ValueCountFrequency (%)
1 54199
77.5%
0 15749
 
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 54199
77.5%
0 15749
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 54199
77.5%
0 15749
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 54199
77.5%
0 15749
 
22.5%

race_Hispanic
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
68457 
1
 
1491

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68457
97.9%
1 1491
 
2.1%

Length

2023-06-04T23:07:22.376007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:22.558733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 68457
97.9%
1 1491
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 68457
97.9%
1 1491
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68457
97.9%
1 1491
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68457
97.9%
1 1491
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68457
97.9%
1 1491
 
2.1%

race_Other
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
68797 
1
 
1151

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68797
98.4%
1 1151
 
1.6%

Length

2023-06-04T23:07:22.711821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:22.895311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 68797
98.4%
1 1151
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 68797
98.4%
1 1151
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68797
98.4%
1 1151
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68797
98.4%
1 1151
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68797
98.4%
1 1151
 
1.6%

age_[10-20)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69419 
1
 
529

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

Length

2023-06-04T23:07:23.049232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:23.233608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

age_[20-30)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
68833 
1
 
1115

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68833
98.4%
1 1115
 
1.6%

Length

2023-06-04T23:07:23.386769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:23.569663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 68833
98.4%
1 1115
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 68833
98.4%
1 1115
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68833
98.4%
1 1115
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68833
98.4%
1 1115
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68833
98.4%
1 1115
 
1.6%

age_[30-40)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
67265 
1
 
2683

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 67265
96.2%
1 2683
 
3.8%

Length

2023-06-04T23:07:23.722377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:23.905005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 67265
96.2%
1 2683
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 67265
96.2%
1 2683
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 67265
96.2%
1 2683
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 67265
96.2%
1 2683
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 67265
96.2%
1 2683
 
3.8%

age_[40-50)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
63170 
1
6778 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 63170
90.3%
1 6778
 
9.7%

Length

2023-06-04T23:07:24.060318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:24.244955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 63170
90.3%
1 6778
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 63170
90.3%
1 6778
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63170
90.3%
1 6778
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 63170
90.3%
1 6778
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63170
90.3%
1 6778
 
9.7%

age_[50-60)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
57629 
1
12319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 57629
82.4%
1 12319
 
17.6%

Length

2023-06-04T23:07:24.400949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:24.585394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 57629
82.4%
1 12319
 
17.6%

Most occurring characters

ValueCountFrequency (%)
0 57629
82.4%
1 12319
 
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 57629
82.4%
1 12319
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 57629
82.4%
1 12319
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 57629
82.4%
1 12319
 
17.6%

age_[60-70)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
54284 
1
15664 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 54284
77.6%
1 15664
 
22.4%

Length

2023-06-04T23:07:24.742186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:24.926784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 54284
77.6%
1 15664
 
22.4%

Most occurring characters

ValueCountFrequency (%)
0 54284
77.6%
1 15664
 
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54284
77.6%
1 15664
 
22.4%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54284
77.6%
1 15664
 
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54284
77.6%
1 15664
 
22.4%

age_[70-80)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
52231 
1
17717 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 52231
74.7%
1 17717
 
25.3%

Length

2023-06-04T23:07:25.083274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:25.267891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 52231
74.7%
1 17717
 
25.3%

Most occurring characters

ValueCountFrequency (%)
0 52231
74.7%
1 17717
 
25.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52231
74.7%
1 17717
 
25.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52231
74.7%
1 17717
 
25.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52231
74.7%
1 17717
 
25.3%

age_[80-90)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
58768 
1
11180 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 58768
84.0%
1 11180
 
16.0%

Length

2023-06-04T23:07:25.424041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:25.608535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 58768
84.0%
1 11180
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 58768
84.0%
1 11180
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58768
84.0%
1 11180
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58768
84.0%
1 11180
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58768
84.0%
1 11180
 
16.0%

age_[90-100)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
68138 
1
 
1810

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 68138
97.4%
1 1810
 
2.6%

Length

2023-06-04T23:07:25.764643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:25.947212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 68138
97.4%
1 1810
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 68138
97.4%
1 1810
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68138
97.4%
1 1810
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68138
97.4%
1 1810
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68138
97.4%
1 1810
 
2.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69941 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Length

2023-06-04T23:07:26.099687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:26.284527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69935 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69935
> 99.9%
1 13
 
< 0.1%

Length

2023-06-04T23:07:26.436452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:26.620830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69935
> 99.9%
1 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69935
> 99.9%
1 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69935
> 99.9%
1 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69935
> 99.9%
1 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69935
> 99.9%
1 13
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
65799 
1
 
4149

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 65799
94.1%
1 4149
 
5.9%

Length

2023-06-04T23:07:26.772633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:26.956604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 65799
94.1%
1 4149
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 65799
94.1%
1 4149
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65799
94.1%
1 4149
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65799
94.1%
1 4149
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65799
94.1%
1 4149
 
5.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69941 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Length

2023-06-04T23:07:27.108238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:27.291719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69944 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%

Length

2023-06-04T23:07:27.443003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:27.628511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69944 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%

Length

2023-06-04T23:07:27.781136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:27.968625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69944
> 99.9%
1 4
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69947 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Length

2023-06-04T23:07:28.123851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:28.311021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
65563 
1
 
4385

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 65563
93.7%
1 4385
 
6.3%

Length

2023-06-04T23:07:28.467585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:28.656381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 65563
93.7%
1 4385
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 65563
93.7%
1 4385
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65563
93.7%
1 4385
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65563
93.7%
1 4385
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65563
93.7%
1 4385
 
6.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69853 
1
 
95

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69853
99.9%
1 95
 
0.1%

Length

2023-06-04T23:07:28.808891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:28.993586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69853
99.9%
1 95
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69853
99.9%
1 95
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69853
99.9%
1 95
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69853
99.9%
1 95
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69853
99.9%
1 95
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69941 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Length

2023-06-04T23:07:29.147371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:29.331504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
64980 
1
 
4968

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 64980
92.9%
1 4968
 
7.1%

Length

2023-06-04T23:07:29.486043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:29.672602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 64980
92.9%
1 4968
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 64980
92.9%
1 4968
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64980
92.9%
1 4968
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64980
92.9%
1 4968
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64980
92.9%
1 4968
 
7.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69568 
1
 
380

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69568
99.5%
1 380
 
0.5%

Length

2023-06-04T23:07:29.828300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:30.014858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69568
99.5%
1 380
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 69568
99.5%
1 380
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69568
99.5%
1 380
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69568
99.5%
1 380
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69568
99.5%
1 380
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69894 
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69894
99.9%
1 54
 
0.1%

Length

2023-06-04T23:07:30.188643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:30.375734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69894
99.9%
1 54
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69894
99.9%
1 54
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69894
99.9%
1 54
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69894
99.9%
1 54
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69894
99.9%
1 54
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69911 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%

Length

2023-06-04T23:07:30.547143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:30.740834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69838 
1
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69838
99.8%
1 110
 
0.2%

Length

2023-06-04T23:07:30.896091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:31.081354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69838
99.8%
1 110
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 69838
99.8%
1 110
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69838
99.8%
1 110
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69838
99.8%
1 110
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69838
99.8%
1 110
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69912 
1
 
36

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%

Length

2023-06-04T23:07:31.234218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:31.417470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69917 
1
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69917
> 99.9%
1 31
 
< 0.1%

Length

2023-06-04T23:07:31.572270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:31.759866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69917
> 99.9%
1 31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69917
> 99.9%
1 31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69917
> 99.9%
1 31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69917
> 99.9%
1 31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69917
> 99.9%
1 31
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
59265 
1
10683 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 59265
84.7%
1 10683
 
15.3%

Length

2023-06-04T23:07:31.916248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:32.102286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 59265
84.7%
1 10683
 
15.3%

Most occurring characters

ValueCountFrequency (%)
0 59265
84.7%
1 10683
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59265
84.7%
1 10683
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59265
84.7%
1 10683
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59265
84.7%
1 10683
 
15.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69132 
1
 
816

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69132
98.8%
1 816
 
1.2%

Length

2023-06-04T23:07:32.261729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:32.451825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69132
98.8%
1 816
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 69132
98.8%
1 816
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69132
98.8%
1 816
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69132
98.8%
1 816
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69132
98.8%
1 816
 
1.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69784 
1
 
164

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69784
99.8%
1 164
 
0.2%

Length

2023-06-04T23:07:32.610646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:32.804520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69784
99.8%
1 164
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 69784
99.8%
1 164
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69784
99.8%
1 164
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69784
99.8%
1 164
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69784
99.8%
1 164
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69947 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Length

2023-06-04T23:07:32.961591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:33.152361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69928 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69928
> 99.9%
1 20
 
< 0.1%

Length

2023-06-04T23:07:33.312663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:33.506887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69928
> 99.9%
1 20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69928
> 99.9%
1 20
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69928
> 99.9%
1 20
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69928
> 99.9%
1 20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69928
> 99.9%
1 20
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69932 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69932
> 99.9%
1 16
 
< 0.1%

Length

2023-06-04T23:07:33.661683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:33.849664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69932
> 99.9%
1 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69932
> 99.9%
1 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69932
> 99.9%
1 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69932
> 99.9%
1 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69932
> 99.9%
1 16
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69354 
1
 
594

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69354
99.2%
1 594
 
0.8%

Length

2023-06-04T23:07:34.004306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:34.194507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69354
99.2%
1 594
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 69354
99.2%
1 594
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69354
99.2%
1 594
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69354
99.2%
1 594
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69354
99.2%
1 594
 
0.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69744 
1
 
204

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69744
99.7%
1 204
 
0.3%

Length

2023-06-04T23:07:34.353206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:34.546172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69744
99.7%
1 204
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 69744
99.7%
1 204
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69744
99.7%
1 204
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69744
99.7%
1 204
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69744
99.7%
1 204
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69913 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69913
99.9%
1 35
 
0.1%

Length

2023-06-04T23:07:34.699765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:34.886128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69913
99.9%
1 35
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69913
99.9%
1 35
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69913
99.9%
1 35
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69913
99.9%
1 35
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69913
99.9%
1 35
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
68832 
1
 
1116

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68832
98.4%
1 1116
 
1.6%

Length

2023-06-04T23:07:35.040463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:35.225967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 68832
98.4%
1 1116
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 68832
98.4%
1 1116
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68832
98.4%
1 1116
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68832
98.4%
1 1116
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68832
98.4%
1 1116
 
1.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
68901 
1
 
1047

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68901
98.5%
1 1047
 
1.5%

Length

2023-06-04T23:07:35.378974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:35.564772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 68901
98.5%
1 1047
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 68901
98.5%
1 1047
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68901
98.5%
1 1047
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68901
98.5%
1 1047
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68901
98.5%
1 1047
 
1.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69911 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%

Length

2023-06-04T23:07:35.717580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:35.901598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69911
99.9%
1 37
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69843 
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69843
99.8%
1 105
 
0.2%

Length

2023-06-04T23:07:36.056215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:36.240370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69843
99.8%
1 105
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 69843
99.8%
1 105
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69843
99.8%
1 105
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69843
99.8%
1 105
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69843
99.8%
1 105
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69940 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69940
> 99.9%
1 8
 
< 0.1%

Length

2023-06-04T23:07:36.394287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:36.578527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69940
> 99.9%
1 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69940
> 99.9%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69940
> 99.9%
1 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69940
> 99.9%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69940
> 99.9%
1 8
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69941 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Length

2023-06-04T23:07:36.732311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:36.916186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69755 
1
 
193

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%

Length

2023-06-04T23:07:37.462743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:37.648413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69947 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Length

2023-06-04T23:07:37.803849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:37.989402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69875 
1
 
73

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69875
99.9%
1 73
 
0.1%

Length

2023-06-04T23:07:38.143755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:38.328958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69875
99.9%
1 73
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69875
99.9%
1 73
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69875
99.9%
1 73
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69875
99.9%
1 73
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69875
99.9%
1 73
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69945 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%

Length

2023-06-04T23:07:38.483547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:38.672346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69805 
1
 
143

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69805
99.8%
1 143
 
0.2%

Length

2023-06-04T23:07:38.830498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:39.018032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69805
99.8%
1 143
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 69805
99.8%
1 143
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69805
99.8%
1 143
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69805
99.8%
1 143
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69805
99.8%
1 143
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69945 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%

Length

2023-06-04T23:07:39.172761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:39.357115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69945
> 99.9%
1 3
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69942 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Length

2023-06-04T23:07:39.513452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:39.699665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69942 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Length

2023-06-04T23:07:39.855696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:40.041834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69947 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Length

2023-06-04T23:07:40.198285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:40.386830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69755 
1
 
193

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%

Length

2023-06-04T23:07:40.546375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:40.734604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69755
99.7%
1 193
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69941 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Length

2023-06-04T23:07:40.896196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:41.087298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69887 
1
 
61

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69887
99.9%
1 61
 
0.1%

Length

2023-06-04T23:07:41.244235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:41.453645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69887
99.9%
1 61
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69887
99.9%
1 61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69887
99.9%
1 61
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69887
99.9%
1 61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69887
99.9%
1 61
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69947 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Length

2023-06-04T23:07:41.614060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:41.809086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69338 
1
 
610

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69338
99.1%
1 610
 
0.9%

Length

2023-06-04T23:07:41.968904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:42.158376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69338
99.1%
1 610
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 69338
99.1%
1 610
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69338
99.1%
1 610
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69338
99.1%
1 610
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69338
99.1%
1 610
 
0.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69947 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Length

2023-06-04T23:07:42.332166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:42.548076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69943 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%

Length

2023-06-04T23:07:42.717703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:42.911614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69943
> 99.9%
1 5
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69896 
1
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69896
99.9%
1 52
 
0.1%

Length

2023-06-04T23:07:43.075084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:43.314306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69896
99.9%
1 52
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69896
99.9%
1 52
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69896
99.9%
1 52
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69896
99.9%
1 52
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69896
99.9%
1 52
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69316 
1
 
632

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69316
99.1%
1 632
 
0.9%

Length

2023-06-04T23:07:43.467130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:43.655708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69316
99.1%
1 632
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 69316
99.1%
1 632
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69316
99.1%
1 632
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69316
99.1%
1 632
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69316
99.1%
1 632
 
0.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69133 
1
 
815

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69133
98.8%
1 815
 
1.2%

Length

2023-06-04T23:07:43.811661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:43.999886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69133
98.8%
1 815
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 69133
98.8%
1 815
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69133
98.8%
1 815
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69133
98.8%
1 815
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69133
98.8%
1 815
 
1.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69912 
1
 
36

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%

Length

2023-06-04T23:07:44.155740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:44.344918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69912
99.9%
1 36
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69946 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Length

2023-06-04T23:07:44.501775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:44.690655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69946
> 99.9%
1 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69937 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69937
> 99.9%
1 11
 
< 0.1%

Length

2023-06-04T23:07:44.846470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:45.033703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69937
> 99.9%
1 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69937
> 99.9%
1 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69937
> 99.9%
1 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69937
> 99.9%
1 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69937
> 99.9%
1 11
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69947 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Length

2023-06-04T23:07:45.186440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:45.370389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69907 
1
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69907
99.9%
1 41
 
0.1%

Length

2023-06-04T23:07:45.524524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:45.708518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69907
99.9%
1 41
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69907
99.9%
1 41
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69907
99.9%
1 41
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69907
99.9%
1 41
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69907
99.9%
1 41
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69860 
1
 
88

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69860
99.9%
1 88
 
0.1%

Length

2023-06-04T23:07:45.862150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:46.047834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69860
99.9%
1 88
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69860
99.9%
1 88
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69860
99.9%
1 88
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69860
99.9%
1 88
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69860
99.9%
1 88
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69464 
1
 
484

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%

Length

2023-06-04T23:07:46.201139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:46.388505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69464
99.3%
1 484
 
0.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69939 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69939
> 99.9%
1 9
 
< 0.1%

Length

2023-06-04T23:07:46.546445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:46.734454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69939
> 99.9%
1 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69939
> 99.9%
1 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69939
> 99.9%
1 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69939
> 99.9%
1 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69939
> 99.9%
1 9
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
67724 
1
 
2224

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 67724
96.8%
1 2224
 
3.2%

Length

2023-06-04T23:07:46.888400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:47.072788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 67724
96.8%
1 2224
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 67724
96.8%
1 2224
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 67724
96.8%
1 2224
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 67724
96.8%
1 2224
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 67724
96.8%
1 2224
 
3.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69941 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Length

2023-06-04T23:07:47.226366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:47.410844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69941
> 99.9%
1 7
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69541 
1
 
407

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69541
99.4%
1 407
 
0.6%

Length

2023-06-04T23:07:47.565078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:47.749005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69541
99.4%
1 407
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 69541
99.4%
1 407
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69541
99.4%
1 407
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69541
99.4%
1 407
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69541
99.4%
1 407
 
0.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69942 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Length

2023-06-04T23:07:47.902636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:48.087802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69942
> 99.9%
1 6
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69918 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%

Length

2023-06-04T23:07:48.243033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:48.427002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69918
> 99.9%
1 30
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69947 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Length

2023-06-04T23:07:48.580958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:48.766222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69947
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69855 
1
 
93

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69855
99.9%
1 93
 
0.1%

Length

2023-06-04T23:07:48.922261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:49.111527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69855
99.9%
1 93
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 69855
99.9%
1 93
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69855
99.9%
1 93
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69855
99.9%
1 93
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69855
99.9%
1 93
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69587 
1
 
361

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69587
99.5%
1 361
 
0.5%

Length

2023-06-04T23:07:49.267844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:49.456797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69587
99.5%
1 361
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 69587
99.5%
1 361
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69587
99.5%
1 361
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69587
99.5%
1 361
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69587
99.5%
1 361
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69922 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69922
> 99.9%
1 26
 
< 0.1%

Length

2023-06-04T23:07:49.613876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:49.799868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69922
> 99.9%
1 26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 69922
> 99.9%
1 26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69922
> 99.9%
1 26
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69922
> 99.9%
1 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69922
> 99.9%
1 26
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
36310 
1
33638 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 36310
51.9%
1 33638
48.1%

Length

2023-06-04T23:07:49.952929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:50.141294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 36310
51.9%
1 33638
48.1%

Most occurring characters

ValueCountFrequency (%)
0 36310
51.9%
1 33638
48.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36310
51.9%
1 33638
48.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36310
51.9%
1 33638
48.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36310
51.9%
1 33638
48.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
69419 
1
 
529

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

Length

2023-06-04T23:07:50.298412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:50.485101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69419
99.2%
1 529
 
0.8%

diag_1_Diabetes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
64272 
1
 
5676

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 64272
91.9%
1 5676
 
8.1%

Length

2023-06-04T23:07:50.638633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:50.824978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 64272
91.9%
1 5676
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 64272
91.9%
1 5676
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64272
91.9%
1 5676
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64272
91.9%
1 5676
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64272
91.9%
1 5676
 
8.1%

diag_1_Digestive
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
63437 
1
6511 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 63437
90.7%
1 6511
 
9.3%

Length

2023-06-04T23:07:50.981039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:51.167389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 63437
90.7%
1 6511
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 63437
90.7%
1 6511
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63437
90.7%
1 6511
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 63437
90.7%
1 6511
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63437
90.7%
1 6511
 
9.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
66474 
1
 
3474

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 66474
95.0%
1 3474
 
5.0%

Length

2023-06-04T23:07:51.323456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:51.509784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 66474
95.0%
1 3474
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 66474
95.0%
1 3474
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66474
95.0%
1 3474
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66474
95.0%
1 3474
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66474
95.0%
1 3474
 
5.0%

diag_1_Injury
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
65213 
1
 
4735

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 65213
93.2%
1 4735
 
6.8%

Length

2023-06-04T23:07:51.662469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:51.846845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 65213
93.2%
1 4735
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 65213
93.2%
1 4735
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65213
93.2%
1 4735
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65213
93.2%
1 4735
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65213
93.2%
1 4735
 
6.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
65909 
1
 
4039

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 65909
94.2%
1 4039
 
5.8%

Length

2023-06-04T23:07:52.001413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:52.189753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 65909
94.2%
1 4039
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 65909
94.2%
1 4039
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65909
94.2%
1 4039
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65909
94.2%
1 4039
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65909
94.2%
1 4039
 
5.8%

diag_1_Neoplasms
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
58725 
1
11223 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 58725
84.0%
1 11223
 
16.0%

Length

2023-06-04T23:07:52.344150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:52.537371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 58725
84.0%
1 11223
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 58725
84.0%
1 11223
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58725
84.0%
1 11223
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58725
84.0%
1 11223
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58725
84.0%
1 11223
 
16.0%

diag_1_Others
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
66463 
1
 
3485

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 66463
95.0%
1 3485
 
5.0%

Length

2023-06-04T23:07:52.695085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:52.877809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 66463
95.0%
1 3485
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 66463
95.0%
1 3485
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66463
95.0%
1 3485
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66463
95.0%
1 3485
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66463
95.0%
1 3485
 
5.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
60492 
1
9456 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 60492
86.5%
1 9456
 
13.5%

Length

2023-06-04T23:07:53.031989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:53.216632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 60492
86.5%
1 9456
 
13.5%

Most occurring characters

ValueCountFrequency (%)
0 60492
86.5%
1 9456
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 60492
86.5%
1 9456
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 60492
86.5%
1 9456
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 60492
86.5%
1 9456
 
13.5%

diag_2_Diabetes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
60131 
1
9817 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 60131
86.0%
1 9817
 
14.0%

Length

2023-06-04T23:07:53.372844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:53.565344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 60131
86.0%
1 9817
 
14.0%

Most occurring characters

ValueCountFrequency (%)
0 60131
86.0%
1 9817
 
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 60131
86.0%
1 9817
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 60131
86.0%
1 9817
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 60131
86.0%
1 9817
 
14.0%

diag_2_Digestive
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
67086 
1
 
2862

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 67086
95.9%
1 2862
 
4.1%

Length

2023-06-04T23:07:53.723390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:53.906466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 67086
95.9%
1 2862
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 67086
95.9%
1 2862
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 67086
95.9%
1 2862
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 67086
95.9%
1 2862
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 67086
95.9%
1 2862
 
4.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
64528 
1
 
5420

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 64528
92.3%
1 5420
 
7.7%

Length

2023-06-04T23:07:54.061051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:54.244885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 64528
92.3%
1 5420
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 64528
92.3%
1 5420
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64528
92.3%
1 5420
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64528
92.3%
1 5420
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64528
92.3%
1 5420
 
7.7%

diag_2_Injury
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
68079 
1
 
1869

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68079
97.3%
1 1869
 
2.7%

Length

2023-06-04T23:07:54.397669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:54.582887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 68079
97.3%
1 1869
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 68079
97.3%
1 1869
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68079
97.3%
1 1869
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68079
97.3%
1 1869
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68079
97.3%
1 1869
 
2.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
68630 
1
 
1318

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68630
98.1%
1 1318
 
1.9%

Length

2023-06-04T23:07:54.736424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:54.921125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 68630
98.1%
1 1318
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 68630
98.1%
1 1318
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68630
98.1%
1 1318
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68630
98.1%
1 1318
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68630
98.1%
1 1318
 
1.9%

diag_2_Neoplasms
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
55689 
1
14259 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 55689
79.6%
1 14259
 
20.4%

Length

2023-06-04T23:07:55.075768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:55.260705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 55689
79.6%
1 14259
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 55689
79.6%
1 14259
 
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55689
79.6%
1 14259
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55689
79.6%
1 14259
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55689
79.6%
1 14259
 
20.4%

diag_2_Others
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
64498 
1
 
5450

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 64498
92.2%
1 5450
 
7.8%

Length

2023-06-04T23:07:55.417175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:55.604878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 64498
92.2%
1 5450
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0 64498
92.2%
1 5450
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64498
92.2%
1 5450
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64498
92.2%
1 5450
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64498
92.2%
1 5450
 
7.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
62976 
1
6972 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 62976
90.0%
1 6972
 
10.0%

Length

2023-06-04T23:07:55.758938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:55.945021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 62976
90.0%
1 6972
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 62976
90.0%
1 6972
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62976
90.0%
1 6972
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 62976
90.0%
1 6972
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 62976
90.0%
1 6972
 
10.0%

diag_3_Diabetes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
56760 
1
13188 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 56760
81.1%
1 13188
 
18.9%

Length

2023-06-04T23:07:56.106060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:56.294596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 56760
81.1%
1 13188
 
18.9%

Most occurring characters

ValueCountFrequency (%)
0 56760
81.1%
1 13188
 
18.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 56760
81.1%
1 13188
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 56760
81.1%
1 13188
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 56760
81.1%
1 13188
 
18.9%

diag_3_Digestive
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
67253 
1
 
2695

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 67253
96.1%
1 2695
 
3.9%

Length

2023-06-04T23:07:56.451230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:56.639259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 67253
96.1%
1 2695
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 67253
96.1%
1 2695
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 67253
96.1%
1 2695
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 67253
96.1%
1 2695
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 67253
96.1%
1 2695
 
3.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
65784 
1
 
4164

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 65784
94.0%
1 4164
 
6.0%

Length

2023-06-04T23:07:56.794444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:57.458889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 65784
94.0%
1 4164
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 65784
94.0%
1 4164
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65784
94.0%
1 4164
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65784
94.0%
1 4164
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65784
94.0%
1 4164
 
6.0%

diag_3_Injury
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
68494 
1
 
1454

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68494
97.9%
1 1454
 
2.1%

Length

2023-06-04T23:07:57.613387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:57.799818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 68494
97.9%
1 1454
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 68494
97.9%
1 1454
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68494
97.9%
1 1454
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68494
97.9%
1 1454
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68494
97.9%
1 1454
 
2.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
68556 
1
 
1392

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68556
98.0%
1 1392
 
2.0%

Length

2023-06-04T23:07:57.953280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:58.138540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 68556
98.0%
1 1392
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 68556
98.0%
1 1392
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68556
98.0%
1 1392
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68556
98.0%
1 1392
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68556
98.0%
1 1392
 
2.0%

diag_3_Neoplasms
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
55546 
1
14402 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 55546
79.4%
1 14402
 
20.6%

Length

2023-06-04T23:07:58.292240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:58.478917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 55546
79.4%
1 14402
 
20.6%

Most occurring characters

ValueCountFrequency (%)
0 55546
79.4%
1 14402
 
20.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55546
79.4%
1 14402
 
20.6%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55546
79.4%
1 14402
 
20.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55546
79.4%
1 14402
 
20.6%

diag_3_Others
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
62868 
1
7080 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 62868
89.9%
1 7080
 
10.1%

Length

2023-06-04T23:07:58.636113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:58.824843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 62868
89.9%
1 7080
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 62868
89.9%
1 7080
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62868
89.9%
1 7080
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 62868
89.9%
1 7080
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 62868
89.9%
1 7080
 
10.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.6 KiB
0
65222 
1
 
4726

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters69948
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 65222
93.2%
1 4726
 
6.8%

Length

2023-06-04T23:07:58.982260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T23:07:59.167023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 65222
93.2%
1 4726
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 65222
93.2%
1 4726
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65222
93.2%
1 4726
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 69948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65222
93.2%
1 4726
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65222
93.2%
1 4726
 
6.8%

Interactions

2023-06-04T23:07:00.876278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:37.619484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:39.966490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:42.150394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:44.291757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:46.529546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:48.744527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:50.925168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:53.214624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:55.492614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:58.696065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:01.078988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:37.820957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:40.175282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:42.347955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:44.505791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:46.738738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:48.944176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:51.131815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:53.439525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:55.697625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:58.901430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:01.266288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:38.017087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:40.357646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:42.529706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:44.695266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:46.933520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:49.145616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:51.326699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:53.633051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:56.884891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:59.096298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:01.453526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:38.232942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:40.541241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:42.710560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:44.883828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:47.129871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:49.345093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:51.523221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:53.822626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:57.074578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:59.284574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:01.659337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:38.440562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:40.745225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:42.904536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:45.075373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:47.327354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:49.538346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:51.730053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:54.023235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:57.269749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:59.477203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:01.870317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:38.652309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:40.943772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:43.098289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:45.267717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:47.529165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:49.729862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:51.940573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:54.226285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:57.466354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:59.673679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:02.063949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:38.869702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:41.133230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:43.291917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:45.488912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:47.733575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:49.920212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:52.147073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:54.447355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:57.655521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:59.866355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:02.272348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:39.107580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:41.337640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:43.502954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:45.701787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:47.940401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:50.127985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:52.360320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:54.660589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:57.871268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:00.073080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:02.479258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:39.336004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:41.553807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:43.710284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:45.916656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:48.149035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:50.333663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:52.576340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:54.872846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:58.092426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:00.278936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:02.672780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:39.542285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:41.757396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:43.900469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:46.116058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:48.343822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:50.526510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:52.781704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:55.081134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:58.287705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:00.477659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:02.871876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:39.754961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:41.951790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:44.095284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:46.321594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:48.544119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:50.724913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:52.991728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:55.284574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:06:58.492808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T23:07:00.674721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-04T23:07:59.678195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
admission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideinsulinglyburide-metforminglipizide-metforminmetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmittednumchangerace_Asianrace_Caucasianrace_Hispanicrace_Otherage_[10-20)age_[20-30)age_[30-40)age_[40-50)age_[50-60)age_[60-70)age_[70-80)age_[80-90)age_[90-100)medical_specialty_Anesthesiologymedical_specialty_Anesthesiology-Pediatricmedical_specialty_Cardiologymedical_specialty_Cardiology-Pediatricmedical_specialty_DCPTEAMmedical_specialty_Dentistrymedical_specialty_Dermatologymedical_specialty_Emergency/Traumamedical_specialty_Endocrinologymedical_specialty_Endocrinology-Metabolismmedical_specialty_Family/GeneralPracticemedical_specialty_Gastroenterologymedical_specialty_Gynecologymedical_specialty_Hematologymedical_specialty_Hematology/Oncologymedical_specialty_Hospitalistmedical_specialty_InfectiousDiseasesmedical_specialty_InternalMedicinemedical_specialty_Nephrologymedical_specialty_Neurologymedical_specialty_Neurophysiologymedical_specialty_Obsterics&Gynecology-GynecologicOncomedical_specialty_Obstetricsmedical_specialty_ObstetricsandGynecologymedical_specialty_Oncologymedical_specialty_Ophthalmologymedical_specialty_Orthopedicsmedical_specialty_Orthopedics-Reconstructivemedical_specialty_Osteopathmedical_specialty_Otolaryngologymedical_specialty_OutreachServicesmedical_specialty_Pathologymedical_specialty_Pediatricsmedical_specialty_Pediatrics-AllergyandImmunologymedical_specialty_Pediatrics-CriticalCaremedical_specialty_Pediatrics-EmergencyMedicinemedical_specialty_Pediatrics-Endocrinologymedical_specialty_Pediatrics-Hematology-Oncologymedical_specialty_Pediatrics-Neurologymedical_specialty_Pediatrics-Pulmonologymedical_specialty_Perinatologymedical_specialty_PhysicalMedicineandRehabilitationmedical_specialty_PhysicianNotFoundmedical_specialty_Podiatrymedical_specialty_Proctologymedical_specialty_Psychiatrymedical_specialty_Psychiatry-Addictivemedical_specialty_Psychiatry-Child/Adolescentmedical_specialty_Psychologymedical_specialty_Pulmonologymedical_specialty_Radiologistmedical_specialty_Radiologymedical_specialty_Residentmedical_specialty_Rheumatologymedical_specialty_Speechmedical_specialty_Surgeonmedical_specialty_Surgery-Cardiovascularmedical_specialty_Surgery-Cardiovascular/Thoracicmedical_specialty_Surgery-Colon&Rectalmedical_specialty_Surgery-Generalmedical_specialty_Surgery-Maxillofacialmedical_specialty_Surgery-Neuromedical_specialty_Surgery-Pediatricmedical_specialty_Surgery-Plasticmedical_specialty_Surgery-PlasticwithinHeadandNeckmedical_specialty_Surgery-Thoracicmedical_specialty_Surgery-Vascularmedical_specialty_SurgicalSpecialtymedical_specialty_Unknowmedical_specialty_Urologydiag_1_Diabetesdiag_1_Digestivediag_1_Genitourinarydiag_1_Injurydiag_1_Muscoloskeletaldiag_1_Neoplasmsdiag_1_Othersdiag_1_Respiratorydiag_2_Diabetesdiag_2_Digestivediag_2_Genitourinarydiag_2_Injurydiag_2_Muscoloskeletaldiag_2_Neoplasmsdiag_2_Othersdiag_2_Respiratorydiag_3_Diabetesdiag_3_Digestivediag_3_Genitourinarydiag_3_Injurydiag_3_Muscoloskeletaldiag_3_Neoplasmsdiag_3_Othersdiag_3_Respiratory
admission_type_id1.0000.029-0.376-0.022-0.2150.2420.1140.039-0.0340.001-0.1280.4030.0880.0540.0400.0230.0080.0560.0000.0230.0110.0120.0300.0320.0000.0040.0000.0150.0920.0400.0000.0000.0000.0510.0350.0240.0270.0270.0970.0490.0230.0250.0050.0010.0190.0250.0610.0240.0690.0540.0140.0000.1460.0190.0040.0000.0000.2370.0150.0000.0860.0370.0500.0320.0580.0400.0110.1840.0240.0480.0000.0190.0390.1160.0480.0360.2020.1390.0100.0530.0070.0190.0190.0000.0090.0000.0140.0000.0000.0000.0000.0650.0000.0350.0000.0310.0000.0000.0270.0620.1410.0180.0000.0110.0110.0270.0490.0890.0100.1270.0100.0890.0000.0260.0110.1030.1150.0180.2730.0900.0670.0760.0190.0620.2550.0470.1000.1370.0400.0200.0390.0570.0640.0650.0450.0470.0520.0200.0160.0530.0320.0610.0220.025
discharge_disposition_id0.0291.0000.0280.2960.0560.0110.1800.0180.0020.0530.1420.0750.0250.0320.0230.0120.0620.0260.0230.0320.0800.0050.0310.0170.0090.0080.0000.0060.1290.0290.0000.0000.0000.0710.0730.1040.0300.0140.0320.0280.0100.0400.0490.0520.0710.0430.0150.0580.0810.0310.0000.0000.0740.0000.0000.0000.0000.0710.0140.0000.0310.0210.0050.0000.0130.0000.0000.0900.0290.0300.0000.0170.0000.0340.0070.0000.1260.0730.0000.0280.0000.0000.0290.0000.0190.0000.0350.0000.0000.0140.0000.0550.0050.0160.0000.0480.0000.0080.0000.0200.0320.0070.0000.0000.0000.0000.0440.0290.0000.0360.0050.0280.0000.0000.0000.0370.0180.0000.1360.0130.0330.0640.0280.1060.1140.0460.0200.0530.0400.0300.0180.0330.0250.0500.0520.0240.0400.0200.0160.0310.0250.0360.0240.021
admission_source_id-0.3760.0281.0000.0150.170-0.212-0.0770.0140.0870.0410.1040.3860.0900.0470.0200.0170.0000.0270.0000.0140.0270.0000.0230.0300.0000.0000.0000.0000.0560.0360.0000.0000.0000.0160.0160.0270.0170.0150.0940.0280.0100.0130.0310.0160.0210.0220.0590.0310.0680.0500.0090.0060.1500.0000.0000.0000.0000.1970.0020.0000.1480.0360.0370.0240.0850.0260.0130.1330.0310.0460.0000.0220.0190.1140.0410.0260.1560.1470.0050.0380.0000.0070.0100.0000.0250.0000.0110.0000.0000.0000.0000.1650.0000.0410.0000.0240.0000.0000.0190.0050.1240.0160.0000.0000.0000.0260.0480.0930.0000.0980.0000.0830.0000.0200.0000.0370.0940.0220.1440.0890.0540.0720.0190.0280.2250.0480.1140.1350.0440.0260.0330.0370.0500.0580.0550.0410.0400.0210.0100.0110.0280.0520.0250.020
time_in_hospital-0.0220.2960.0151.0000.3500.1760.464-0.017-0.0050.0810.2450.0320.0530.0200.0350.0000.0060.0190.0240.0270.0310.0000.0060.0130.0080.0050.0070.0000.1120.0000.0000.0000.0000.1190.0750.0670.0850.0190.0080.0160.0140.0520.0390.0470.0540.0510.0080.0470.0830.0430.0040.0000.0970.0000.0160.0000.0000.0170.0000.0000.0040.0000.0190.0020.0110.0000.0000.0330.0180.0100.0000.0160.0050.0530.0150.0170.0730.0610.0000.0260.0020.0250.0210.0310.0170.0000.0370.0000.0000.0120.0000.0870.0000.0050.0110.0670.0140.0000.0150.0290.0420.0050.0000.0000.0000.0000.0280.0620.0120.0060.0000.0310.0110.0010.0000.0170.0210.0020.0140.0290.0240.0160.0180.0420.0980.0620.0460.0390.1030.0210.0280.0440.0100.0350.0440.0630.0920.0080.0570.0590.0140.0320.0320.068
num_lab_procedures-0.2150.0560.1700.3501.0000.0030.242-0.0200.0200.0750.1690.2580.1910.0550.0260.0140.0000.0200.0040.0270.0140.0000.0150.0040.0000.0000.0000.0000.1120.0140.0000.0000.0000.0820.0570.0350.0710.0130.0600.0000.0210.0200.0280.0170.0180.0320.0270.0110.0420.0260.0000.0090.0580.0010.0000.0000.0000.0740.0190.0000.0430.0150.0400.0080.0520.0000.0000.1650.0380.0410.0000.0000.0260.0740.0200.0160.1090.0720.0110.0380.0000.0100.0000.0000.0190.0000.0250.0000.0000.0000.0000.0220.0000.0140.0000.0270.0040.0000.0100.0340.0670.0120.0000.0000.0000.0090.0160.0330.0000.0520.0030.0560.0020.0160.0000.0160.0510.0130.0470.0620.0460.0460.0570.0540.1420.0400.0440.0430.0650.0170.0770.0170.0400.0390.0450.0470.0690.0180.0820.0110.0210.0500.0390.049
num_procedures0.2420.011-0.2120.1760.0031.0000.368-0.016-0.040-0.0260.0680.0570.0590.0470.0060.0080.0010.0100.0150.0090.0120.0000.0110.0150.0000.0030.0000.0060.0390.0040.0000.0060.0000.0300.0310.0250.0220.0110.0510.0210.0120.0680.0410.0330.0120.0590.0770.0210.0890.0710.0080.0070.2290.0060.0000.0000.0070.0380.0130.0000.0780.0140.0240.0100.0130.0000.0000.1050.0440.0180.0000.0250.0150.0720.0190.0240.1230.1320.0080.0320.0000.0130.0240.0000.0310.0000.0500.0000.0120.0020.0000.0170.0090.0230.0150.0630.0030.0020.0260.0270.1520.0180.0000.0000.0000.0130.0380.0970.0080.1140.0000.0680.0000.0160.0070.0500.0680.0080.0800.0720.1060.1280.0500.1310.1920.0990.0740.2100.0610.0740.0360.0810.0320.1250.0870.0520.0240.0530.0250.0610.0190.0850.0450.010
num_medications0.1140.180-0.0770.4640.2420.3681.0000.0600.0300.0840.2940.0340.0170.0800.0270.0280.0070.0480.0230.0690.0570.0000.0770.0590.0240.0080.0000.0000.2010.0090.0000.0430.0000.2420.1950.0630.1120.0340.0650.0360.0160.1060.0710.0540.0310.0270.0740.0460.0490.0380.0000.0070.0510.0150.0000.0000.0000.0640.0120.0000.0630.0190.0060.0000.0020.0000.0000.0680.0240.0000.0000.0000.0000.0150.0110.0140.0930.0810.0000.0000.0090.0000.0430.0000.0350.0050.0820.0000.0000.0000.0000.0210.0120.0010.0000.0480.0000.0000.0160.0050.0410.0000.0000.0000.0000.0000.0550.3050.0000.0270.0000.0530.0000.0000.0000.0670.0450.0020.0570.0090.1010.0490.0340.0570.1540.0640.0570.0540.0970.0180.0250.0580.0280.0770.0300.0880.0670.0160.0450.0640.0130.0520.0330.081
number_outpatient0.0390.0180.014-0.017-0.020-0.0160.0601.0000.1610.1210.0990.0080.0060.0110.0040.0000.0000.0000.0000.0000.0010.0000.0040.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0160.0070.0140.0100.0000.0230.0060.0000.0000.0110.0080.0110.0090.0020.0030.0130.0000.0090.0000.0110.0000.0000.0160.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0390.0000.0080.0100.0000.0000.0000.0000.0360.0000.0140.0000.0120.0000.0000.0000.0230.0090.0100.0000.0180.0000.0050.0070.0120.000
number_emergency-0.0340.0020.087-0.0050.020-0.0400.0300.1611.0000.1740.0810.0010.0000.0040.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0070.0000.0000.0000.0020.0070.0310.0000.0000.0090.0000.0000.0000.0230.0260.0220.0020.0080.0090.0040.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0160.0000.0000.0000.0160.0030.0000.0090.0000.0000.0000.0000.0040.0170.0110.0000.0000.0130.0000.0240.0250.0110.0000.000
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A1Cresult0.0880.0250.0900.0530.1910.0590.0170.0060.0000.0220.0550.0461.0000.0530.0220.0000.0000.0270.0000.0230.0200.0000.0000.0110.0030.0000.0000.0000.1170.0030.0000.0030.0000.1130.0980.0230.0960.0060.0390.0310.0110.0790.0420.0360.0580.0320.0150.0530.0540.0240.0000.0190.0210.0090.0000.0000.0000.0410.0330.0000.0140.0070.0110.0000.0090.0070.0000.0620.0050.0010.0000.0030.0050.0160.0180.0020.0390.0310.0000.0070.0060.0000.0290.0000.0600.0160.0770.0000.0100.0150.0000.0110.0000.0060.0000.0190.0000.0050.0030.0160.0300.0000.0000.0000.0000.0070.0100.0070.0000.0270.0000.0200.0000.0010.0000.0000.0200.0080.0470.0290.1530.0440.0140.0480.0520.0110.0360.0150.0600.0240.0140.0230.0040.0410.0380.0060.0190.0140.0000.0120.0000.0540.0230.005
metformin0.0540.0320.0470.0200.0550.0470.0800.0110.0040.0470.0720.0290.0531.0000.0000.0140.0090.0400.0000.0740.1460.0060.0570.0940.0100.0090.0000.0000.0220.0250.0000.0000.0000.3490.2890.0280.1190.0000.0000.0120.0000.0300.0250.0020.0350.0520.0400.0140.0720.0520.0060.0000.0340.0000.0000.0000.0000.0070.0140.0000.0050.0000.0000.0060.0000.0020.0050.0260.0460.0150.0000.0000.0000.0100.0000.0120.0570.0360.0000.0110.0000.0000.0000.0000.0130.0000.0160.0000.0000.0000.0000.0130.0000.0110.0000.0310.0000.0000.0130.0040.0100.0050.0000.0050.0000.0020.0160.0000.0020.0000.0000.0230.0000.0060.0000.0090.0000.0000.0000.0170.0110.0570.0150.0000.0870.0350.0020.0210.0470.0260.0420.0210.0310.0160.0120.0000.0400.0160.0490.0090.0220.0070.0060.006
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chlorpropamide0.0080.0620.0000.0060.0000.0010.0070.0000.0000.0000.0130.0070.0000.0090.0000.0001.0000.0050.0000.0090.0040.0000.0050.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0160.0040.0000.0000.0100.0000.0000.0000.0000.0000.0050.0080.0000.0090.0030.0030.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0020.0010.0000.0060.0000.0050.0000.0000.0040.0000.0000.0000.0050.0030.0000.0100.0000.0000.0000.0000.0090.0000.000
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glyburide0.0110.0800.0270.0310.0140.0120.0570.0010.0000.0190.0340.0000.0200.1460.0210.0220.0040.0740.0000.1121.0000.0000.0230.0330.0170.0000.0000.0050.0680.0080.0000.0000.0000.1970.1960.0030.0790.0000.0290.0060.0050.0290.0330.0310.0350.0230.0000.0460.0330.0000.0000.0000.0180.0000.0000.0000.0000.0300.0060.0000.0040.0000.0000.0000.0130.0060.0000.0160.0220.0060.0030.0000.0000.0160.0000.0000.0150.0140.0060.0060.0000.0000.0090.0000.0100.0000.0140.0000.0000.0000.0000.0140.0000.0000.0000.0030.0000.0000.0000.0070.0020.0080.0000.0000.0000.0000.0080.0060.0000.0050.0000.0110.0000.0000.0000.0000.0030.0040.0170.0050.0330.0410.0110.0060.0270.0160.0030.0030.0000.0270.0120.0020.0000.0240.0000.0070.0040.0180.0150.0090.0000.0230.0040.000
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glipizide-metformin0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0301.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.000
metformin-rosiglitazone0.0000.0000.0000.0000.0000.0060.0430.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
metformin-pioglitazone0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
change0.0510.0710.0160.1190.0820.0300.2420.0160.0020.0140.0540.0530.1130.3490.0730.0570.0000.1440.0000.2100.1970.0000.2140.2060.0470.0110.0000.0000.5040.0440.0070.0000.0001.0000.5070.0260.6360.0080.0100.0050.0140.0050.0040.0000.0190.0140.0270.0070.0360.0300.0000.0060.0270.0000.0000.0030.0000.0630.0020.0000.0000.0080.0030.0060.0020.0070.0000.0100.0200.0000.0000.0000.0040.0290.0000.0050.0080.0000.0000.0000.0000.0000.0020.0000.0190.0000.0250.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0040.0060.0070.0050.0000.0000.0000.0000.0070.0250.0010.0150.0000.0020.0060.0000.0000.0130.0120.0060.0000.0140.0820.0830.0120.0020.0160.0000.0180.0150.0310.0380.0010.0060.0070.0170.0130.0170.0000.0280.0030.0100.0000.0230.0000.013
diabetesMed0.0350.0730.0160.0750.0570.0310.1950.0070.0070.0240.0360.0560.0980.2890.0640.0470.0160.1320.0000.2140.1960.0070.1590.1480.0300.0070.0000.0100.5740.0460.0000.0000.0000.5071.0000.0350.3220.0050.0040.0000.0060.0190.0020.0130.0000.0070.0180.0030.0190.0300.0000.0050.0140.0000.0000.0000.0000.0220.0080.0000.0020.0000.0100.0000.0000.0080.0000.0080.0130.0010.0000.0000.0100.0260.0030.0000.0080.0040.0030.0000.0000.0050.0000.0000.0160.0000.0170.0000.0010.0000.0000.0040.0000.0120.0000.0000.0000.0000.0000.0040.0000.0040.0000.0000.0000.0000.0050.0210.0060.0240.0000.0000.0060.0000.0000.0060.0000.0000.0070.0150.0720.0770.0090.0020.0180.0090.0230.0160.0280.0430.0000.0000.0070.0100.0070.0100.0090.0310.0000.0030.0000.0030.0060.009
readmitted0.0240.1040.0270.0670.0350.0250.0630.0140.0310.2380.0790.0160.0230.0280.0190.0000.0040.0040.0000.0060.0030.0000.0030.0050.0000.0000.0000.0000.0420.0000.0000.0000.0000.0260.0351.0000.0460.0040.0040.0060.0090.0170.0040.0110.0210.0340.0000.0250.0340.0070.0000.0000.0280.0000.0000.0000.0000.0060.0050.0000.0080.0000.0090.0100.0180.0000.0040.0000.0260.0060.0000.0000.0000.0210.0130.0000.0000.0180.0000.0110.0000.0000.0100.0000.0080.0000.0160.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0040.0120.0000.0000.0000.0100.0000.0000.0000.0000.0060.0010.0180.0030.0080.0070.0000.0110.0170.0000.0020.0160.0210.0000.0100.0000.0020.0130.0060.0000.0200.0000.0240.0000.0010.0020.0050.017
numchange0.0270.0300.0170.0850.0710.0220.1120.0100.0000.0150.0420.0410.0960.1190.0590.0060.0000.0540.0000.0690.0790.0000.0660.0580.0180.0190.0000.0040.4460.0060.0000.0000.0000.6360.3220.0461.0000.0000.0280.0170.0290.0270.0480.0320.0300.0000.0030.0280.0260.0170.0000.0000.0370.0000.0000.0000.0000.0930.0000.0000.0030.0140.0000.0110.0050.0000.0190.0140.0000.0150.0000.0000.0000.0150.0000.0000.0170.0170.0050.0060.0000.0000.0030.0000.0060.0000.0130.0000.0000.0000.0000.0090.0000.0130.0000.0100.0000.0500.0010.0000.0070.0000.0060.0000.0000.0000.0040.0190.0000.0120.0000.0080.0000.0000.0000.0190.0080.0070.0050.0220.1370.0470.0040.0070.0320.0030.0160.0050.0220.0160.0100.0000.0110.0310.0160.0150.0180.0120.0250.0000.0080.0390.0020.017
race_Asian0.0270.0140.0150.0190.0130.0110.0340.0000.0000.0000.0160.0020.0060.0000.0070.0120.0000.0020.0000.0040.0000.0000.0000.0050.0000.0000.0000.0000.0120.0100.0000.0000.0000.0080.0050.0040.0001.0000.1560.0110.0090.0000.0000.0040.0000.0000.0000.0040.0030.0000.0000.0000.0100.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0100.0130.0030.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0060.0000.0000.0000.0270.0000.0000.0000.0040.0000.0000.0000.0000.0050.0110.0000.0000.0000.0000.0000.0010.0030.0000.0260.0000.0090.0070.0050.0000.0050.0080.0030.0040.0000.0140.0000.0000.0000.0000.0000.0040.0000.0110.0020.0000.0000.0000.0000.000
race_Caucasian0.0970.0320.0940.0080.0600.0510.0650.0230.0090.0120.0940.0400.0390.0000.0080.0130.0100.0140.0000.0090.0290.0030.0220.0140.0100.0000.0000.0000.0470.0000.0000.0000.0000.0100.0040.0040.0280.1561.0000.2740.2400.0250.0530.0780.0820.0670.0000.0720.1020.0310.0000.0010.0280.0000.0000.0000.0000.0160.0000.0000.0310.0080.0000.0000.0060.0040.0000.0910.0510.0050.0000.0010.0000.0240.0080.0000.0270.0130.0080.0040.0020.0000.0000.0000.0080.0000.0050.0000.0000.0000.0000.0010.0000.0000.0000.0070.0000.0040.0000.0070.0430.0030.0000.0070.0000.0010.0000.0000.0000.0100.0000.0030.0000.0000.0000.0000.0230.0030.0590.0030.0750.0060.0000.0110.0430.0050.0190.0000.0340.0040.0090.0140.0120.0250.0000.0390.0130.0050.0050.0070.0000.0080.0000.022
race_Hispanic0.0490.0280.0280.0160.0000.0210.0360.0060.0000.0000.0440.0250.0310.0120.0070.0000.0000.0080.0000.0000.0060.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0060.0170.0110.2741.0000.0180.0100.0220.0420.0290.0130.0000.0240.0360.0160.0000.0000.0150.0000.0000.0000.0000.0270.0000.0000.0240.0030.0000.0000.0030.0000.0000.0080.0090.0000.0000.0000.0000.0220.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0040.0100.0000.0000.0000.0120.0000.0010.0110.0000.0000.0000.0000.0000.0000.0000.0000.0020.0030.0170.0050.0100.0120.0000.0070.0100.0000.0170.0110.0220.0050.0000.0000.0000.0060.0110.0010.0160.0110.0020.0000.0000.0000.0030.007
race_Other0.0230.0100.0100.0140.0210.0120.0160.0000.0000.0110.0180.0000.0110.0000.0050.0050.0000.0000.0000.0080.0050.0000.0060.0050.0000.0000.0000.0000.0110.0090.0000.0000.0000.0140.0060.0090.0290.0090.2400.0181.0000.0000.0000.0090.0050.0130.0150.0120.0230.0090.0000.0000.0000.0000.0000.0000.0000.0460.0000.0000.0030.0000.0050.0140.0000.0000.0000.0100.0060.0050.0000.0000.0000.0040.0000.0000.0060.0060.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0050.0000.0090.0000.0000.0010.0000.0000.0000.0050.0000.0070.0000.0000.0000.0080.0040.0050.0060.0000.0000.0000.0000.0020.0090.0060.0000.0070.0040.0020.0020.0070.0000.0000.0000.0010.0050.0000.0000.0060.0040.0000.0000.000
age_[10-20)0.0250.0400.0130.0520.0200.0680.1060.0000.0000.0100.2550.0120.0790.0300.0070.0020.0000.0180.0000.0310.0290.0000.0230.0200.0000.0000.0000.0000.0550.0050.0000.0000.0000.0050.0190.0170.0270.0000.0250.0100.0001.0000.0100.0170.0280.0400.0470.0510.0380.0130.0000.1380.0210.0570.0000.0000.0000.0060.0170.0000.0090.0040.0000.0000.0000.0000.0000.0220.0060.0000.0000.0000.0000.0100.0000.0000.0100.0080.0000.0000.0000.0000.1790.0210.2400.0620.3050.0620.0000.0970.0000.0000.0000.0000.0000.0080.0000.0080.0120.0000.0080.0000.0000.0000.0000.0000.0000.0050.0000.0090.0000.0000.0610.0000.0000.0000.0030.0280.0240.0050.2030.0210.0130.0100.0190.0160.0010.0230.0370.0000.0130.0040.0080.0540.0110.0190.0140.0000.0130.0000.0090.0410.0250.002
age_[20-30)0.0050.0490.0310.0390.0280.0410.0710.0110.0230.0470.1130.0160.0420.0250.0090.0080.0000.0190.0000.0330.0330.0000.0260.0210.0040.0000.0000.0000.0470.0090.0000.0000.0000.0040.0020.0040.0480.0000.0530.0220.0000.0101.0000.0250.0410.0590.0680.0740.0550.0200.0000.0000.0290.0000.0000.0210.0000.0050.0000.0020.0050.0000.0000.0000.0000.0000.0000.0000.0110.0030.0000.0000.0090.1900.0040.0000.0130.0110.0000.0000.0010.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0040.0000.0000.0000.0190.0000.0000.0150.0000.0120.0000.0000.0000.0000.0010.0000.0080.0000.0040.0000.0050.0000.0000.0000.0000.0060.0360.0080.0040.1240.0100.0040.0120.0240.0000.1170.0310.0520.0010.0100.0000.0100.0300.0540.0250.0160.0090.0060.0000.0030.0260.0580.014
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medical_specialty_Anesthesiology0.0140.0000.0090.0040.0000.0080.0000.0090.0000.0000.0000.0000.0000.0060.0000.0070.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0140.0000.0030.0000.0000.0000.0000.0000.0130.0000.0000.000
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medical_specialty_Cardiology0.1460.0740.1500.0970.0580.2290.0510.0110.0000.0120.0600.0140.0210.0340.0000.0000.0000.0120.0000.0240.0180.0000.0070.0130.0000.0000.0020.0030.0460.0040.0000.0000.0000.0270.0140.0280.0370.0100.0280.0150.0000.0210.0290.0320.0100.0230.0340.0120.0250.0250.0000.0001.0000.0000.0000.0000.0000.0650.0080.0000.0690.0180.0050.0020.0080.0020.0010.1060.0270.0110.0000.0000.0000.0230.0120.0020.0320.0300.0020.0080.0000.0000.0120.0000.0060.0000.0100.0000.0000.0000.0000.0120.0000.0050.0000.0230.0000.0000.0040.0230.0270.0020.0000.0000.0000.0030.0070.0200.0000.0450.0000.0180.0000.0000.0000.0070.0170.0000.2420.0210.0640.0630.0480.0430.0570.0830.0360.0170.0140.0380.0400.0140.0240.0800.0310.0190.0050.0280.0250.0000.0210.0490.0200.004
medical_specialty_Cardiology-Pediatric0.0190.0000.0000.0000.0010.0060.0150.0000.0000.0000.0220.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0060.0000.000
medical_specialty_DCPTEAM0.0040.0000.0000.0160.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Dentistry0.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Dermatology0.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.000
medical_specialty_Emergency/Trauma0.2370.0710.1970.0170.0740.0380.0640.0200.0160.0220.0540.0570.0410.0070.0810.0110.0060.0450.0000.0020.0300.0000.0000.0090.0060.0150.0000.0010.0500.0300.0000.0000.0000.0630.0220.0060.0930.0410.0160.0270.0460.0060.0050.0000.0100.0040.0120.0190.0300.0240.0000.0000.0650.0000.0000.0000.0001.0000.0080.0000.0710.0180.0050.0030.0090.0030.0010.1100.0280.0110.0000.0000.0000.0230.0130.0020.0320.0310.0030.0080.0000.0000.0120.0000.0060.0000.0100.0000.0000.0000.0000.0120.0000.0050.0000.0240.0000.0000.0050.0240.0280.0030.0000.0000.0000.0030.0070.0210.0000.0470.0000.0190.0000.0010.0000.0080.0180.0000.2490.0220.0000.0160.0000.0100.0430.0090.0210.0320.0140.0200.0000.0040.0000.0270.0180.0110.0170.0000.0000.0060.0000.0330.0120.009
medical_specialty_Endocrinology0.0150.0140.0020.0000.0190.0130.0120.0000.0000.0000.0210.0060.0330.0140.0000.0000.0000.0050.0000.0000.0060.0000.0000.0020.0000.0000.0000.0000.0190.0000.0000.0000.0000.0020.0080.0050.0000.0000.0000.0000.0000.0170.0000.0070.0100.0000.0090.0020.0000.0000.0000.0000.0080.0000.0000.0000.0000.0081.0000.0000.0090.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0320.0000.0000.0000.0030.0000.0000.0050.0120.0030.0000.0000.0000.0100.0000.0030.0000.0000.0000.0000.0010.0050.0000.007
medical_specialty_Endocrinology-Metabolism0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Family/GeneralPractice0.0860.0310.1480.0040.0430.0780.0630.0000.0000.0060.0530.0740.0140.0050.0030.0050.0000.0030.0000.0020.0040.0000.0100.0120.0000.0060.0000.0000.0090.0000.0000.0000.0000.0000.0020.0080.0030.0000.0310.0240.0030.0090.0050.0050.0160.0060.0190.0050.0000.0070.0000.0000.0690.0000.0000.0000.0000.0710.0090.0001.0000.0200.0060.0030.0100.0030.0020.1170.0300.0120.0000.0000.0000.0250.0140.0030.0350.0340.0030.0090.0000.0000.0130.0000.0070.0000.0110.0000.0000.0000.0000.0130.0000.0060.0000.0250.0000.0000.0050.0260.0300.0030.0000.0000.0000.0040.0080.0220.0000.0500.0000.0200.0000.0020.0000.0090.0190.0010.2660.0240.0310.0160.0060.0330.0450.0140.0150.0390.0170.0080.0080.0160.0090.0180.0130.0130.0000.0120.0000.0120.0000.0170.0130.007
medical_specialty_Gastroenterology0.0370.0210.0360.0000.0150.0140.0190.0000.0000.0190.0000.0060.0070.0000.0020.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0020.0030.0000.0000.0000.0080.0000.0000.0140.0000.0080.0030.0000.0040.0000.0050.0000.0030.0000.0000.0000.0020.0000.0000.0180.0000.0000.0000.0000.0180.0000.0000.0201.0000.0000.0000.0000.0000.0000.0310.0060.0000.0000.0000.0000.0040.0000.0000.0080.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0050.0060.0000.0000.0000.0000.0000.0000.0030.0000.0120.0000.0020.0000.0000.0000.0000.0010.0000.0710.0040.0070.0460.0010.0070.0170.0160.0000.0070.0000.0230.0090.0000.0050.0120.0040.0000.0000.0120.0020.0050.0000.0030.0030.000
medical_specialty_Gynecology0.0500.0050.0370.0190.0400.0240.0060.0000.0000.0000.0150.0030.0110.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0050.0000.0000.0000.0000.0110.0060.0000.0000.0000.0030.0100.0090.0000.0000.0000.0000.0050.0000.0000.0280.0120.0020.0060.0000.0090.0000.0000.0000.0050.0000.0000.0000.0000.0050.0000.0000.0060.0001.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0060.0070.0520.0050.0040.0070.0230.0100.0000.0000.0250.0000.0000.0060.0110.0050.0060.0000.0290.0000.0000.0000.0000.005
medical_specialty_Hematology0.0320.0000.0240.0020.0080.0100.0000.0000.0000.0270.0000.0000.0000.0060.0100.0000.0000.0060.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0100.0110.0000.0000.0000.0140.0000.0000.0000.0020.0000.0050.0020.0000.0000.0000.0000.0020.0000.0000.0000.0000.0030.0000.0000.0030.0000.0001.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0030.0020.0100.0270.0000.0080.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0080.0000.000
medical_specialty_Hematology/Oncology0.0580.0130.0850.0110.0520.0130.0020.0000.0000.0000.0180.0900.0090.0000.0000.0000.0000.0000.0000.0000.0130.0000.0030.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0020.0000.0180.0050.0000.0060.0030.0000.0000.0000.0030.0030.0000.0000.0080.0030.0000.0000.0000.0080.0000.0000.0000.0000.0090.0000.0000.0100.0000.0000.0001.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0060.0000.0000.0080.0000.0170.0210.0030.0050.0000.0000.0000.0020.0260.0090.0000.0000.0090.0000.0020.0000.0090.0000.000
medical_specialty_Hospitalist0.0400.0000.0260.0000.0000.0000.0000.0000.0000.0000.0060.0000.0070.0020.0000.0000.0000.0060.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0070.0080.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0020.0000.0000.0000.0000.0030.0000.0000.0030.0000.0000.0000.0001.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0030.0000.0020.0070.0000.0000.0020.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.004
medical_specialty_InfectiousDiseases0.0110.0000.0130.0000.0000.0000.0000.0000.0000.0060.0110.0030.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0190.0100.0000.0000.0000.0000.0000.0030.0040.0000.0010.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0010.0000.0000.0020.0000.0000.0000.0000.0001.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0060.0000.0000.0000.0000.0110.0000.0040.0050.0000.0000.0000.0090.0000.0000.0000.0000.0000.0010.0000.0020.0000.0090.000
medical_specialty_InternalMedicine0.1840.0900.1330.0330.1650.1050.0680.0200.0080.0480.0940.0220.0620.0260.0030.0050.0000.0000.0000.0190.0160.0000.0100.0040.0080.0000.0000.0000.0320.0170.0000.0000.0000.0100.0080.0000.0140.0130.0910.0080.0100.0220.0000.0010.0000.0170.0310.0130.0320.0280.0000.0020.1060.0000.0000.0000.0000.1100.0150.0000.1170.0310.0100.0080.0160.0080.0071.0000.0460.0200.0000.0050.0030.0390.0220.0080.0540.0520.0080.0150.0000.0000.0220.0000.0130.0000.0180.0000.0000.0000.0000.0220.0000.0110.0000.0390.0000.0000.0100.0400.0460.0080.0000.0000.0000.0090.0140.0350.0000.0770.0000.0320.0000.0070.0000.0140.0300.0060.4090.0370.0490.0190.0040.0410.0700.0000.0020.0440.0100.0090.0100.0330.0100.0220.0130.0060.0000.0070.0060.0110.0000.0190.0150.000
medical_specialty_Nephrology0.0240.0290.0310.0180.0380.0440.0240.0000.0000.0320.0310.0030.0050.0460.0020.0000.0000.0020.0000.0050.0220.0000.0000.0130.0000.0000.0000.0000.0110.0030.0000.0000.0000.0200.0130.0260.0000.0030.0510.0090.0060.0060.0110.0080.0000.0150.0140.0060.0190.0070.0000.0000.0270.0000.0000.0000.0000.0280.0000.0000.0300.0060.0000.0000.0000.0000.0000.0461.0000.0010.0000.0000.0000.0090.0030.0000.0130.0120.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0090.0000.0000.0000.0090.0110.0000.0000.0000.0000.0000.0000.0070.0000.0190.0000.0060.0000.0000.0000.0000.0060.0000.1040.0080.0300.0050.0170.0310.0210.0000.0100.0190.0160.0080.0140.0000.0060.0120.0080.0210.0000.0100.0310.0000.0060.0160.0020.005
medical_specialty_Neurology0.0480.0300.0460.0100.0410.0180.0000.0000.0000.0000.0300.0080.0010.0150.0000.0110.0000.0000.0000.0100.0060.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0010.0060.0150.0000.0050.0000.0050.0000.0030.0000.0040.0020.0000.0000.0060.0030.0000.0000.0110.0000.0000.0000.0000.0110.0000.0000.0120.0000.0000.0000.0000.0000.0000.0200.0011.0000.0000.0000.0000.0000.0000.0000.0030.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0460.0000.0050.0130.0100.0090.0910.0080.0150.0170.0100.0070.0060.0000.0270.0000.0120.0030.0110.0000.0070.0000.0210.0060.0080.007
medical_specialty_Neurophysiology0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Obsterics&Gynecology-GynecologicOnco0.0190.0170.0220.0160.0000.0250.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0020.0020.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0040.0000.0000.0230.0000.0040.0000.0000.0050.0000.0000.0060.0000.0000.0000.0000.0070.0000.0000.0000.0000.000
medical_specialty_Obstetrics0.0390.0000.0190.0050.0260.0150.0000.0000.0000.0000.0160.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0040.0100.0000.0000.0000.0000.0000.0000.0000.0090.0190.0050.0000.0000.0040.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0250.0000.0000.0000.0200.0030.0110.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0080.000
medical_specialty_ObstetricsandGynecology0.1160.0340.1140.0530.0740.0720.0150.0340.0000.0140.0480.0120.0160.0100.0080.0060.0000.0110.0000.0180.0160.0000.0160.0140.0000.0000.0000.0000.0080.0000.0000.0000.0000.0290.0260.0210.0150.0000.0240.0220.0040.0100.1900.1210.0210.0190.0340.0400.0350.0130.0000.0000.0230.0000.0000.0000.0000.0230.0000.0000.0250.0040.0000.0000.0000.0000.0000.0390.0090.0000.0000.0000.0001.0000.0000.0000.0110.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0070.0090.0000.0000.0000.0000.0000.0000.0060.0000.0160.0000.0050.0000.0000.0000.0000.0040.0000.0890.0060.0260.0270.0740.0170.0220.0080.2130.0360.0430.0080.0210.0000.0090.0280.0980.0250.0240.0090.0290.0000.0080.0250.0780.019
medical_specialty_Oncology0.0480.0070.0410.0150.0200.0190.0110.0000.0000.0130.0030.0110.0180.0000.0020.0000.0000.0000.0000.0020.0000.0000.0020.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0030.0130.0000.0000.0080.0000.0000.0000.0040.0050.0050.0000.0120.0000.0000.0070.0000.0000.0120.0000.0000.0000.0000.0130.0000.0000.0140.0000.0000.0000.0000.0000.0000.0220.0030.0000.0000.0000.0000.0001.0000.0000.0040.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0080.0000.0000.0030.0090.0240.0450.0030.0100.0030.0040.0000.0050.0450.0080.0100.0080.0000.0050.0030.0030.0250.0050.000
medical_specialty_Ophthalmology0.0360.0000.0260.0170.0160.0240.0140.0000.0000.0000.0040.0030.0020.0120.0000.0000.0000.0000.0000.0010.0000.0000.0030.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0020.0000.0040.0000.0000.0000.0020.0000.0000.0000.0000.0020.0000.0000.0030.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0040.0010.0000.0100.0420.0080.0130.0050.0170.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0040.0140.003
medical_specialty_Orthopedics0.2020.1260.1560.0730.1090.1230.0930.0000.0000.0200.0610.0250.0390.0570.0000.0000.0120.0000.0000.0000.0150.0000.0140.0210.0000.0000.0000.0040.0310.0270.0040.0000.0000.0080.0080.0000.0170.0000.0270.0030.0060.0100.0130.0080.0130.0000.0250.0130.0160.0130.0000.0000.0320.0000.0000.0000.0000.0320.0000.0000.0350.0080.0000.0000.0000.0000.0000.0540.0130.0030.0000.0000.0000.0110.0040.0001.0000.0150.0000.0000.0000.0000.0040.0000.0000.0000.0020.0000.0000.0000.0000.0040.0000.0000.0000.0110.0000.0000.0000.0110.0130.0000.0000.0000.0000.0000.0000.0090.0000.0220.0000.0080.0000.0000.0000.0000.0070.0000.1220.0100.0330.0400.0290.0730.3800.0530.0210.0500.0300.0170.0200.0340.0840.0300.0330.0130.0300.0020.0180.0110.0430.0230.0070.017
medical_specialty_Orthopedics-Reconstructive0.1390.0730.1470.0610.0720.1320.0810.0000.0000.0110.0590.0460.0310.0360.0000.0000.0000.0000.0000.0090.0140.0000.0150.0170.0000.0000.0000.0050.0350.0000.0000.0000.0000.0000.0040.0180.0170.0040.0130.0000.0060.0080.0110.0120.0060.0030.0110.0100.0080.0040.0000.0000.0300.0000.0000.0000.0000.0310.0000.0000.0340.0070.0000.0000.0000.0000.0000.0520.0120.0030.0000.0000.0000.0100.0040.0000.0151.0000.0000.0000.0000.0000.0040.0000.0000.0000.0020.0000.0000.0000.0000.0040.0000.0000.0000.0100.0000.0000.0000.0100.0120.0000.0000.0000.0000.0000.0000.0090.0000.0220.0000.0080.0000.0000.0000.0000.0070.0000.1180.0090.0250.0380.0270.1190.3000.0480.0210.0470.0390.0180.0190.0320.0520.0270.0360.0170.0300.0100.0170.0180.0440.0200.0160.016
medical_specialty_Osteopath0.0100.0000.0050.0000.0110.0080.0000.0000.1660.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0020.0060.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0030.0000.0050.0000.0080.0000.0000.0000.0000.0000.0000.0000.0020.0000.0070.0000.0000.0000.0020.0000.0000.0000.0000.0030.0000.0000.0030.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0010.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.006
medical_specialty_Otolaryngology0.0530.0280.0380.0260.0380.0320.0000.0000.0000.0000.0370.0030.0070.0110.0000.0000.0030.0000.0000.0020.0060.0000.0000.0070.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0110.0060.0000.0040.0000.0000.0000.0000.0000.0000.0090.0020.0000.0130.0040.0000.0000.0080.0000.0000.0000.0000.0080.0000.0000.0090.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0090.0020.0070.0060.0080.0470.0000.0100.0120.0000.0070.0000.0000.0050.0000.0050.0180.0060.0080.0000.0000.0030.0030.005
medical_specialty_OutreachServices0.0070.0000.0000.0020.0000.0000.0090.0000.0000.0000.0000.0000.0060.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Pathology0.0190.0000.0070.0250.0100.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Pediatrics0.0190.0290.0100.0210.0000.0240.0430.0000.0000.0170.0930.0100.0290.0000.0030.0000.0000.0070.0000.0070.0090.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0100.0030.0000.0000.0000.0000.1790.0000.0000.0000.0030.0140.0210.0180.0050.0000.0000.0120.0000.0000.0000.0000.0120.0000.0000.0130.0000.0000.0000.0000.0000.0000.0220.0020.0000.0000.0000.0000.0000.0000.0000.0040.0040.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0660.0090.0030.0050.0080.0010.0000.0060.0250.0000.0080.0020.0020.0190.0000.0050.0110.0000.0070.0080.0000.0050.0000.000
medical_specialty_Pediatrics-AllergyandImmunology0.0000.0000.0000.0310.0000.0000.0000.0000.0000.0140.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Pediatrics-CriticalCare0.0090.0190.0250.0170.0190.0310.0350.0000.0000.0000.1520.0050.0600.0130.0000.0000.0000.0050.0000.0110.0100.0000.0070.0070.0000.0000.0000.0000.0300.0000.0000.0000.0000.0190.0160.0080.0060.0040.0080.0000.0000.2400.0000.0040.0090.0140.0160.0180.0130.0010.0000.0000.0060.0000.0000.0000.0000.0060.0000.0000.0070.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.1080.0090.0050.0070.0060.0130.0050.0120.0000.0000.0040.0010.0000.0350.0000.0060.0000.0000.0040.0000.0000.0230.0100.000
medical_specialty_Pediatrics-EmergencyMedicine0.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0190.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0020.0000.0000.0000.0000.0000.000
medical_specialty_Pediatrics-Endocrinology0.0140.0350.0110.0370.0250.0500.0820.0000.0000.0020.2740.0090.0770.0160.0000.0000.0000.0090.0000.0160.0140.0000.0100.0110.0000.0000.0000.0000.0370.0000.0000.0000.0000.0250.0170.0160.0130.0000.0050.0000.0000.3050.0020.0070.0140.0200.0240.0260.0190.0050.0000.0000.0100.0000.0000.0000.0000.0100.0000.0000.0110.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0020.0020.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.1480.0120.0090.0110.0100.0180.0090.0160.0390.0000.0080.0050.0030.0240.0000.0120.0280.0030.0060.0040.0040.0190.0000.007
medical_specialty_Pediatrics-Hematology-Oncology0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Pediatrics-Neurology0.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0020.0000.0090.000
medical_specialty_Pediatrics-Pulmonology0.0000.0140.0000.0120.0000.0020.0000.0000.0000.0220.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.012
medical_specialty_Perinatology0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0030.000
medical_specialty_PhysicalMedicineandRehabilitation0.0650.0550.1650.0870.0220.0170.0210.0000.0000.0000.0140.0100.0110.0130.0030.0000.0000.0000.0000.0060.0140.0000.0000.0100.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0040.0070.0090.0000.0010.0060.0000.0000.0040.0000.0070.0000.0000.0210.0000.0050.0000.0000.0120.0000.0000.0000.0000.0120.0000.0000.0130.0000.0000.0000.0000.0000.0000.0220.0020.0000.0000.0000.0000.0000.0000.0000.0040.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0140.0160.0090.0120.0000.0220.2140.0200.0040.0060.0040.0360.0320.0090.0030.0000.0030.0060.0000.0030.0120.0070.0300.011
medical_specialty_PhysicianNotFound0.0000.0050.0000.0000.0000.0090.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.000
medical_specialty_Podiatry0.0350.0160.0410.0050.0140.0230.0010.0170.0000.0000.0160.0040.0060.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0170.0120.0000.0130.0000.0000.0000.0000.0000.0000.0000.0040.0000.0070.0050.0070.0000.0000.0000.0050.0000.0000.0000.0000.0050.0000.0000.0060.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0470.0080.0040.0120.0120.0060.0040.0100.0020.0000.0040.0000.0220.0270.0000.0080.0000.0030.0000.0000.0250.0100.0020.000
medical_specialty_Proctology0.0000.0000.0000.0110.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.000
medical_specialty_Psychiatry0.0310.0480.0240.0670.0270.0630.0480.0000.0000.0150.0350.0230.0190.0310.0000.0050.0000.0000.0000.0100.0030.0000.0000.0090.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0100.0060.0070.0000.0020.0080.0190.0330.0430.0140.0150.0300.0170.0080.0000.0000.0230.0000.0000.0000.0000.0240.0000.0000.0250.0050.0000.0000.0000.0000.0000.0390.0090.0000.0000.0000.0000.0070.0000.0000.0110.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0070.0090.0000.0000.0000.0000.0000.0000.0060.0000.0160.0000.0050.0000.0000.0000.0000.0040.0000.0900.0060.0270.0290.0210.0230.0230.2010.0030.0350.0190.0130.0090.0120.0040.0590.0000.0120.0130.0110.0120.0070.0000.0390.0020.000
medical_specialty_Psychiatry-Addictive0.0000.0000.0000.0140.0040.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Psychiatry-Child/Adolescent0.0000.0080.0000.0000.0000.0020.0000.0000.0000.0000.0120.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0040.0000.0000.0080.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0170.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Psychology0.0270.0000.0190.0150.0100.0260.0160.0000.0550.0000.0210.0030.0030.0130.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0010.0000.0000.0000.0000.0120.0150.0060.0270.0000.0000.0150.0110.0000.0000.0000.0040.0000.0000.0000.0000.0050.0000.0000.0050.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0060.0070.0030.0050.0040.0600.0000.0090.0120.0000.0030.0000.0000.0150.0060.0000.0000.0000.0040.0000.0080.0090.0000.002
medical_specialty_Pulmonology0.0620.0200.0050.0290.0340.0270.0050.0000.0000.0040.0210.0200.0160.0040.0090.0000.0000.0000.0000.0000.0070.0000.0050.0140.0000.0000.0000.0000.0060.0000.0000.0000.0000.0060.0040.0000.0000.0270.0070.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0240.0000.0000.0260.0050.0000.0000.0000.0000.0000.0400.0090.0000.0000.0000.0000.0070.0000.0000.0110.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0001.0000.0090.0000.0000.0000.0000.0000.0000.0060.0000.0160.0000.0050.0000.0000.0000.0000.0040.0000.0920.0060.0000.0030.0070.0080.0120.0000.0040.0560.0000.0000.0120.0080.0000.0070.0030.0470.0000.0000.0120.0000.0020.0000.0000.021
medical_specialty_Radiologist0.1410.0320.1240.0420.0670.1520.0410.0000.0000.0180.0190.0230.0300.0100.0000.0000.0000.0070.0000.0000.0020.0000.0150.0000.0000.0000.0000.0000.0000.0090.0000.0000.0170.0070.0000.0060.0070.0000.0430.0100.0050.0080.0120.0150.0120.0000.0260.0110.0140.0150.0000.0000.0270.0000.0000.0000.0000.0280.0000.0000.0300.0060.0000.0000.0000.0000.0000.0460.0110.0010.0000.0000.0000.0090.0030.0000.0130.0120.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0090.0000.0000.0000.0091.0000.0000.0000.0000.0000.0000.0000.0070.0000.0190.0000.0060.0000.0000.0000.0000.0060.0000.1040.0080.0240.0220.0220.0060.0410.0350.0170.0270.0130.0100.0150.0150.0040.0310.0270.0120.0070.0100.0100.0000.0000.0160.0020.000
medical_specialty_Radiology0.0180.0070.0160.0050.0120.0180.0000.0000.0000.0000.0120.0000.0000.0050.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0040.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0030.0000.0000.0030.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0050.0050.0000.0000.0020.0000.0000.0020.0050.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0020.000
medical_specialty_Resident0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.000
medical_specialty_Rheumatology0.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0070.0000.0000.0000.0000.0000.0000.0000.0040.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Speech0.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Surgeon0.0270.0000.0260.0000.0090.0130.0000.0000.0000.0000.0050.0010.0070.0020.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0010.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0030.0000.0000.0040.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0080.0070.0000.0090.0060.0000.0000.0000.0080.0000.0000.0110.0000.0000.0000.0030.0000.0000.0000.0100.000
medical_specialty_Surgery-Cardiovascular0.0490.0440.0480.0280.0160.0380.0550.0000.0000.0000.0000.0060.0100.0160.0000.0020.0000.0040.0000.0000.0080.0000.0020.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0050.0040.0040.0000.0000.0010.0000.0000.0000.0010.0040.0080.0070.0000.0090.0020.0000.0000.0070.0000.0000.0000.0000.0070.0000.0000.0080.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0090.0100.0060.0000.0000.0050.0070.0080.0000.0020.0000.0000.0000.0020.0000.0000.0000.0010.0000.0000.0000.0030.0050.000
medical_specialty_Surgery-Cardiovascular/Thoracic0.0890.0290.0930.0620.0330.0970.3050.0000.0000.0120.0360.0180.0070.0000.0000.0050.0040.0010.0000.0220.0060.0000.0000.0010.0000.0000.0000.0000.0290.0050.0000.0000.0000.0250.0210.0120.0190.0000.0000.0110.0000.0050.0080.0090.0000.0100.0300.0000.0230.0130.0000.0000.0200.0000.0000.0000.0000.0210.0000.0000.0220.0030.0000.0000.0000.0000.0000.0350.0070.0000.0000.0000.0000.0060.0000.0000.0090.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0060.0070.0000.0000.0000.0000.0000.0001.0000.0000.0140.0000.0040.0000.0000.0000.0000.0030.0000.0800.0050.0190.0230.0180.0000.0200.0190.0160.0260.0000.0150.0150.0160.0060.0280.0010.0000.0000.0090.0100.0040.0030.0240.0000.009
medical_specialty_Surgery-Colon&Rectal0.0100.0000.0000.0120.0000.0080.0000.0000.0000.0000.0070.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0060.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0060.0000.0000.0000.0130.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.000
medical_specialty_Surgery-General0.1270.0360.0980.0060.0520.1140.0270.0000.0240.0060.0530.0420.0270.0000.0000.0000.0000.0050.0000.0120.0050.0000.0050.0000.0000.0000.0000.0000.0210.0010.0000.0000.0000.0150.0240.0000.0120.0110.0100.0000.0050.0090.0040.0080.0000.0100.0100.0030.0230.0070.0000.0000.0450.0000.0000.0000.0000.0470.0040.0000.0500.0120.0000.0000.0050.0000.0000.0770.0190.0070.0000.0000.0000.0160.0080.0000.0220.0220.0000.0050.0000.0000.0080.0000.0030.0000.0060.0000.0000.0000.0000.0080.0000.0010.0000.0160.0000.0000.0000.0160.0190.0000.0000.0000.0000.0000.0040.0140.0001.0000.0000.0130.0000.0000.0000.0040.0120.0000.1740.0150.0090.0860.0210.0320.0230.0300.0170.0520.0160.0440.0180.0360.0000.0000.0040.0170.0220.0100.0090.0130.0000.0100.0040.007
medical_specialty_Surgery-Maxillofacial0.0100.0050.0000.0000.0030.0000.0000.0000.0000.0000.0020.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
medical_specialty_Surgery-Neuro0.0890.0280.0830.0310.0560.0680.0530.0000.0000.0110.0890.0160.0200.0230.0030.0000.0000.0000.0000.0000.0110.0000.0030.0110.0000.0000.0000.0110.0160.0000.0000.0000.0000.0020.0000.0100.0080.0000.0030.0000.0070.0000.0050.0010.0090.0170.0100.0040.0210.0070.0000.0000.0180.0000.0000.0000.0000.0190.0000.0000.0200.0020.0000.0000.0000.0000.0000.0320.0060.0000.0000.0000.0000.0050.0000.0000.0080.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0050.0060.0000.0000.0000.0000.0000.0000.0040.0000.0130.0001.0000.0000.0000.0000.0000.0020.0000.0730.0040.0210.0240.0170.0000.1950.0130.0040.0300.0490.0130.0090.0080.0550.0180.0000.0110.0300.0080.0090.0000.0270.0180.0000.008
medical_specialty_Surgery-Pediatric0.0000.0000.0000.0110.0020.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0060.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0050.0000.0000.0060.0000.0000.0000.0020.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.000
medical_specialty_Surgery-Plastic0.0260.0000.0200.0010.0160.0160.0000.0000.0000.0000.0100.0000.0010.0060.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0070.0050.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0020.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0190.0000.0000.0040.0000.0120.0000.0160.0000.0060.0000.0000.0030.0150.0000.0000.0040.0000.0030.0000.0000.0080.0000.0030.0000.000
medical_specialty_Surgery-PlasticwithinHeadandNeck0.0110.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.005
medical_specialty_Surgery-Thoracic0.1030.0370.0370.0170.0160.0500.0670.0000.0000.0410.0050.0060.0000.0090.0020.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0130.0060.0000.0190.0010.0000.0000.0080.0000.0000.0050.0000.0000.0000.0020.0000.0000.0000.0000.0070.0000.0000.0000.0000.0080.0000.0000.0090.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0001.0000.0000.0000.0350.0000.0020.0100.0060.0000.0070.0000.0010.0000.0010.0030.0040.0000.0000.0090.0010.0130.0000.0020.0000.0000.0000.0040.0040.005
medical_specialty_Surgery-Vascular0.1150.0180.0940.0210.0510.0680.0450.0000.0000.0000.0000.0150.0200.0000.0000.0030.0000.0030.0000.0000.0030.0000.0030.0140.0000.0000.0000.0000.0100.0000.0000.0000.0000.0120.0000.0060.0080.0030.0230.0020.0040.0030.0060.0050.0060.0030.0070.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0180.0000.0000.0190.0010.0000.0000.0000.0000.0000.0300.0060.0000.0000.0000.0000.0040.0000.0000.0070.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0040.0060.0000.0000.0000.0000.0000.0000.0030.0000.0120.0000.0020.0000.0000.0000.0001.0000.0000.0690.0030.0290.0170.0140.0280.0110.0140.0100.0270.0040.0020.0040.0090.0000.0000.0090.0140.0000.0080.0000.0020.0000.0010.0000.004
medical_specialty_SurgicalSpecialty0.0180.0000.0220.0020.0130.0080.0020.0270.0000.0000.0050.0000.0080.0000.0000.0000.0000.0000.0000.0010.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0010.0070.0000.0030.0030.0050.0280.0360.0130.0030.0070.0050.0060.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0170.0000.0020.0030.0000.0000.0000.0100.0480.0050.0070.0000.0000.0000.0000.0000.0200.0000.0060.0000.0000.0000.0000.0000.0190.000
medical_specialty_Unknow0.2730.1360.1440.0140.0470.0800.0570.0390.0080.0000.1870.0200.0470.0000.0370.0120.0000.0330.0000.0230.0170.0020.0040.0260.0010.0090.0000.0000.0020.0000.0000.0000.0000.0000.0070.0180.0050.0260.0590.0170.0060.0240.0080.0100.0100.0090.0000.0050.0230.0050.0070.0110.2420.0070.0040.0040.0000.2490.0350.0070.2660.0710.0260.0210.0380.0210.0190.4090.1040.0460.0000.0150.0130.0890.0520.0210.1220.1180.0210.0370.0080.0070.0500.0000.0300.0020.0430.0020.0060.0060.0000.0500.0070.0280.0000.0900.0000.0050.0250.0920.1040.0210.0000.0100.0000.0220.0340.0800.0090.1740.0070.0730.0060.0190.0000.0350.0690.0171.0000.0840.0120.0250.0190.0110.0700.0000.0050.0260.0500.0070.0490.0000.0110.0080.0000.0220.0360.0160.0270.0080.0160.0070.0020.010
medical_specialty_Urology0.0900.0130.0890.0290.0620.0720.0090.0000.0000.0000.0520.0050.0290.0170.0000.0000.0000.0000.0000.0000.0050.0000.0000.0040.0000.0000.0000.0000.0330.0000.0000.0000.0000.0140.0150.0030.0220.0000.0030.0050.0000.0050.0040.0060.0000.0000.0100.0000.0030.0110.0000.0000.0210.0000.0000.0000.0000.0220.0000.0000.0240.0040.0000.0000.0000.0000.0000.0370.0080.0000.0000.0000.0000.0060.0000.0000.0100.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0060.0080.0000.0000.0000.0000.0000.0000.0050.0000.0150.0000.0040.0000.0000.0000.0000.0030.0000.0841.0000.0230.0220.1790.0000.0210.0480.0110.0320.0160.0080.0800.0140.0050.0150.0110.0130.0150.0020.0570.0000.0000.0200.0050.009
diag_1_Diabetes0.0670.0330.0540.0240.0460.1060.1010.0080.0160.0280.1950.0310.1530.0110.0000.0000.0070.0050.0000.0310.0330.0000.0180.0060.0030.0000.0000.0000.1420.0100.0000.0000.0000.0820.0720.0080.1370.0090.0750.0100.0000.2030.1240.0920.0600.0000.0470.0680.0570.0190.0000.0440.0640.0260.0000.0000.0000.0000.0320.0000.0310.0070.0060.0000.0060.0000.0060.0490.0300.0050.0000.0000.0000.0260.0080.0040.0330.0250.0000.0090.0020.0000.0660.0000.1080.0180.1480.0000.0000.0000.0000.0140.0000.0470.0000.0270.0000.0000.0060.0000.0240.0050.0000.0000.0000.0000.0090.0190.0000.0090.0000.0210.0000.0000.0000.0020.0290.0020.0120.0231.0000.0950.0680.0800.0730.1300.0680.1170.0820.0000.0000.0320.0360.1890.0070.0490.0990.0150.0000.0180.0460.1440.0200.024
diag_1_Digestive0.0760.0640.0720.0160.0460.1280.0490.0100.0000.0000.0050.0180.0440.0570.0190.0110.0020.0230.0000.0460.0410.0000.0310.0280.0040.0000.0000.0000.0130.0070.0000.0000.0000.0830.0770.0070.0470.0070.0060.0120.0000.0210.0100.0030.0250.0050.0000.0090.0030.0000.0000.0000.0630.0000.0000.0000.0000.0160.0000.0080.0160.0460.0070.0000.0000.0000.0000.0190.0050.0130.0000.0000.0000.0270.0000.0010.0400.0380.0000.0020.0000.0000.0090.0000.0090.0000.0120.0000.0000.0000.0000.0160.0000.0080.0000.0290.0000.0000.0070.0030.0220.0050.0000.0000.0000.0000.0100.0230.0060.0860.0000.0240.0000.0040.0000.0100.0170.0030.0250.0220.0951.0000.0730.0860.0790.1400.0730.1270.0110.2330.0070.0180.0310.0090.0630.0330.0130.1570.0110.0220.0220.0010.0000.011
diag_1_Genitourinary0.0190.0280.0190.0180.0570.0500.0340.0000.0000.0000.0360.0100.0140.0150.0020.0080.0010.0070.0000.0010.0110.0000.0000.0080.0000.0000.0000.0000.0060.0020.0000.0000.0000.0120.0090.0000.0040.0050.0000.0000.0000.0130.0040.0000.0000.0120.0110.0000.0220.0180.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0060.0010.0520.0000.0000.0030.0000.0040.0170.0100.0000.0040.0250.0740.0000.0000.0290.0270.0010.0070.0000.0000.0030.0000.0050.0000.0090.0000.0000.0000.0000.0090.0000.0040.0070.0210.0000.0000.0030.0070.0220.0000.0000.0000.0000.0000.0060.0180.0000.0210.0000.0170.0000.0000.0000.0060.0140.0000.0190.1790.0680.0731.0000.0610.0560.1000.0520.0900.0010.0180.1120.0110.0150.0700.0230.0370.0090.0070.0890.0160.0050.0360.0200.016
diag_1_Injury0.0620.1060.0280.0420.0540.1310.0570.0000.0000.0000.0450.0120.0480.0000.0020.0000.0000.0000.0000.0000.0060.0080.0150.0030.0000.0000.0000.0000.0000.0140.0070.0000.0000.0020.0020.0110.0070.0000.0110.0070.0020.0100.0120.0120.0070.0130.0040.0000.0320.0250.0000.0000.0430.0000.0000.0000.0000.0100.0000.0000.0330.0070.0050.0030.0080.0000.0000.0410.0310.0090.0000.0000.0000.0170.0030.0100.0730.1190.0000.0060.0000.0000.0050.0000.0070.0000.0110.0000.0000.0000.0000.0120.0000.0120.0000.0230.0000.0000.0050.0080.0060.0000.0000.0000.0000.0080.0000.0000.0000.0320.0000.0000.0000.0120.0050.0000.0280.0000.0110.0000.0800.0860.0611.0000.0660.1180.0610.1060.0200.0220.0000.1670.0040.0170.0930.0340.0190.0270.0000.1400.0000.0340.0900.018
diag_1_Muscoloskeletal0.2550.1140.2250.0980.1420.1920.1540.0000.0160.0300.0870.0210.0520.0870.0050.0010.0060.0040.0000.0170.0270.0000.0350.0300.0000.0040.0020.0070.0540.0220.0000.0000.0060.0160.0180.0170.0320.0050.0430.0100.0090.0190.0240.0200.0080.0090.0360.0210.0310.0290.0190.0000.0570.0000.0000.0000.0000.0430.0030.0000.0450.0170.0040.0020.0000.0020.0000.0700.0210.0910.0000.0000.0000.0220.0090.0420.3800.3000.0000.0080.0000.0000.0080.0000.0060.0000.0100.0000.0000.0000.0000.0000.0000.0120.0000.0230.0000.0000.0040.0120.0410.0020.0000.0080.0000.0070.0000.0200.0000.0230.0000.1950.0000.0000.0000.0070.0110.0000.0700.0210.0730.0790.0560.0661.0000.1080.0560.0980.0690.0320.0280.0280.1540.0480.0440.0240.0540.0130.0240.0020.1040.0330.0000.029
diag_1_Neoplasms0.0470.0460.0480.0620.0400.0990.0640.0000.0030.0000.0120.0140.0110.0350.0000.0040.0000.0080.0000.0020.0160.0000.0000.0000.0030.0000.0000.0000.0110.0000.0000.0000.0000.0000.0090.0000.0030.0080.0050.0000.0060.0160.0000.0240.0370.0180.0180.0260.0000.0000.0000.0020.0830.0000.0020.0020.0000.0090.0000.0000.0140.0160.0070.0100.0170.0070.0110.0000.0000.0080.0000.0230.0000.0080.0240.0080.0530.0480.0000.0470.0000.0000.0010.0000.0130.0000.0180.0000.0000.0000.0000.0220.0040.0060.0000.2010.0000.0170.0600.0000.0350.0000.0000.0000.0000.0000.0050.0190.0130.0300.0000.0130.0050.0160.0000.0000.0140.0100.0000.0480.1300.1400.1000.1180.1081.0000.1000.1730.0380.0120.0500.0000.0160.1030.0200.0180.0070.0120.0260.0150.0070.0620.0090.003
diag_1_Others0.1000.0200.1140.0460.0440.0740.0570.0360.0000.0320.0130.0150.0360.0020.0000.0000.0050.0050.0000.0080.0030.0000.0070.0080.0000.0000.0000.0000.0210.0000.0000.0000.0000.0180.0230.0020.0160.0030.0190.0170.0000.0010.1170.0910.0110.0290.0220.0140.0000.0090.0000.0000.0360.0000.0000.0000.0000.0210.0000.0000.0150.0000.0230.0270.0210.0000.0000.0020.0100.0150.0000.0000.0200.2130.0450.0130.0210.0210.0000.0000.0000.0340.0000.0000.0050.0000.0090.0000.0080.0000.0070.2140.0000.0040.0000.0030.0000.0000.0000.0040.0170.0000.0000.0000.0000.0090.0070.0160.0000.0170.0000.0040.0000.0000.0000.0010.0100.0480.0050.0110.0680.0730.0520.0610.0560.1001.0000.0900.0140.0210.0100.0000.0290.0000.1000.0290.0090.0080.0040.0110.0070.0130.0900.019
diag_1_Respiratory0.1370.0530.1350.0390.0430.2100.0540.0000.0090.0080.0290.0310.0150.0210.0000.0000.0000.0070.0010.0100.0030.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0160.0160.0050.0040.0000.0110.0070.0230.0310.0150.0110.0090.0060.0020.0120.0160.0000.0010.0170.0000.0000.0000.0000.0320.0050.0000.0390.0070.0100.0000.0030.0000.0040.0440.0190.0170.0000.0040.0030.0360.0030.0050.0500.0470.0030.0100.0000.0000.0060.0010.0120.0000.0160.0000.0000.0210.0000.0200.0000.0100.0000.0350.0000.0000.0090.0560.0270.0020.0000.0000.0000.0060.0080.0260.0000.0520.0000.0300.0000.0060.0000.0000.0270.0050.0260.0320.1170.1270.0900.1060.0980.1730.0901.0000.0280.0340.0340.0520.0240.0270.0660.1610.0210.0270.0240.0370.0130.0050.0380.063
diag_2_Diabetes0.0400.0400.0440.1030.0650.0610.0970.0140.0000.0250.3210.0170.0600.0470.0050.0000.0000.0120.0000.0060.0000.0000.0000.0160.0030.0000.0000.0030.0230.0110.0000.0000.0010.0310.0280.0210.0220.0000.0340.0220.0040.0370.0520.0720.0700.0470.0110.0590.0750.0390.0000.0000.0140.0000.0000.0000.0000.0140.0120.0000.0170.0000.0000.0080.0050.0020.0050.0100.0160.0100.0000.0000.0110.0430.0100.0170.0300.0390.0000.0120.0000.0000.0250.0000.0000.0000.0390.0000.0000.0000.0000.0040.0000.0020.0000.0190.0000.0010.0120.0000.0130.0050.0000.0000.0000.0000.0000.0000.0000.0160.0000.0490.0060.0000.0000.0010.0040.0070.0500.0160.0820.0110.0010.0200.0690.0380.0140.0281.0000.0830.1170.0670.0560.2040.1170.1340.0920.0000.0300.0280.0110.0310.0440.039
diag_2_Digestive0.0200.0300.0260.0210.0170.0740.0180.0000.0000.0070.0210.0070.0240.0260.0070.0050.0040.0100.0000.0270.0270.0000.0180.0190.0050.0000.0000.0000.0020.0000.0000.0000.0000.0380.0430.0000.0160.0140.0040.0050.0020.0000.0010.0100.0140.0140.0000.0150.0120.0070.0000.0000.0380.0000.0000.0000.0000.0200.0030.0000.0080.0230.0000.0000.0000.0000.0000.0090.0080.0070.0000.0000.0000.0080.0030.0000.0170.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0060.0000.0000.0080.0130.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0020.0150.0060.0440.0000.0130.0000.0000.0000.0030.0020.0000.0070.0080.0000.2330.0180.0220.0320.0120.0210.0340.0831.0000.0600.0340.0280.1040.0600.0690.0090.1320.0160.0060.0090.0000.0240.016
diag_2_Genitourinary0.0390.0180.0330.0280.0770.0360.0250.0120.0000.0010.1010.0020.0140.0420.0050.0020.0000.0000.0000.0050.0120.0000.0000.0080.0030.0000.0000.0030.0210.0040.0000.0000.0000.0010.0000.0100.0100.0000.0090.0000.0020.0130.0100.0050.0200.0260.0110.0120.0430.0250.0000.0000.0400.0000.0000.0000.0000.0000.0000.0030.0080.0090.0250.0000.0000.0000.0000.0100.0140.0060.0000.0050.0000.0210.0040.0000.0200.0190.0000.0070.0000.0000.0080.0000.0040.0000.0080.0000.0000.0000.0000.0040.0000.0040.0000.0090.0000.0000.0030.0120.0150.0000.0000.0000.0000.0000.0000.0150.0000.0180.0000.0090.0000.0030.0000.0040.0040.0000.0490.0800.0000.0070.1120.0000.0280.0500.0100.0340.1170.0601.0000.0480.0400.1470.0840.0960.0330.0030.0880.0000.0090.0240.0090.008
diag_2_Injury0.0570.0330.0370.0440.0170.0810.0580.0000.0000.0070.0460.0100.0230.0210.0000.0030.0000.0020.0000.0000.0020.0000.0090.0050.0000.0000.0000.0000.0000.0030.0010.0000.0000.0060.0000.0000.0000.0000.0140.0000.0070.0040.0000.0040.0000.0000.0060.0030.0040.0090.0000.0000.0140.0000.0000.0000.0000.0040.0000.0000.0160.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0340.0320.0000.0000.0000.0000.0020.0000.0010.0000.0050.0000.0000.0000.0000.0360.0000.0000.0000.0120.0000.0000.0000.0080.0150.0000.0000.0000.0000.0080.0000.0160.0000.0360.0000.0080.0000.0150.0000.0000.0090.0000.0000.0140.0320.0180.0110.1670.0280.0000.0000.0520.0670.0340.0481.0000.0220.0840.0480.0550.0270.0180.0090.1450.0000.0380.0460.004
diag_2_Muscoloskeletal0.0640.0250.0500.0100.0400.0320.0280.0000.0040.0000.0170.0110.0040.0310.0000.0000.0000.0060.0000.0110.0000.0000.0060.0000.0000.0060.0000.0000.0170.0090.0000.0000.0000.0070.0070.0020.0110.0000.0120.0000.0000.0080.0100.0060.0120.0150.0000.0000.0100.0070.0140.0000.0240.0000.0000.0000.0000.0000.0000.0000.0090.0050.0000.0000.0020.0000.0090.0100.0060.0270.0000.0000.0000.0090.0050.0000.0840.0520.0000.0000.0000.0000.0020.0000.0000.0000.0030.0000.0000.0000.0000.0320.0000.0220.0000.0040.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0550.0020.0000.0000.0000.0000.0000.0110.0050.0360.0310.0150.0040.1540.0160.0290.0240.0560.0280.0400.0221.0000.0700.0400.0460.0160.0150.0160.0000.1500.0070.0000.018
diag_2_Neoplasms0.0650.0500.0580.0350.0390.1250.0770.0000.0170.0160.0250.0120.0410.0160.0060.0000.0050.0110.0000.0110.0240.0060.0050.0000.0000.0000.0000.0050.0320.0000.0000.0000.0000.0170.0100.0130.0310.0000.0250.0060.0000.0540.0300.0370.0520.0100.0270.0440.0190.0050.0000.0200.0800.0100.0000.0000.0000.0270.0100.0000.0180.0120.0060.0230.0260.0000.0000.0220.0120.0000.0000.0060.0000.0280.0450.0020.0300.0270.0000.0050.0000.0000.0190.0000.0350.0100.0240.0000.0000.0120.0000.0090.0000.0270.0000.0590.0000.0000.0150.0070.0310.0000.0000.0000.0000.0000.0020.0280.0000.0000.0000.0180.0000.0000.0000.0090.0000.0000.0080.0150.1890.0090.0700.0170.0480.1030.0000.0270.2040.1040.1470.0840.0701.0000.1470.1680.0000.0110.0000.0080.0000.1230.0150.004
diag_2_Others0.0450.0520.0550.0440.0450.0870.0300.0230.0110.0090.0300.0070.0380.0120.0110.0000.0030.0130.0000.0010.0000.0010.0080.0000.0000.0000.0000.0000.0200.0050.0000.0000.0000.0130.0070.0060.0160.0000.0000.0110.0000.0110.0540.0390.0090.0170.0030.0040.0000.0000.0030.0000.0310.0000.0000.0000.0000.0180.0000.0000.0130.0040.0110.0000.0090.0000.0000.0130.0080.0120.0000.0000.0110.0980.0080.0000.0330.0360.0000.0000.0020.0030.0000.0000.0000.0000.0000.0000.0000.0000.0050.0030.0000.0000.0000.0000.0000.0000.0060.0030.0270.0040.0000.0000.0000.0110.0000.0010.0000.0040.0000.0000.0000.0040.0000.0010.0090.0200.0000.0110.0070.0630.0230.0930.0440.0200.1000.0660.1170.0600.0840.0480.0400.1471.0000.0970.0310.0240.0200.0040.0090.0250.0700.026
diag_2_Respiratory0.0470.0240.0410.0630.0470.0520.0880.0090.0000.0200.0890.0220.0060.0000.0060.0030.0000.0100.0030.0110.0070.0000.0010.0040.0000.0000.0000.0000.0150.0000.0000.0000.0000.0170.0100.0000.0150.0040.0390.0010.0010.0190.0250.0310.0300.0110.0130.0270.0180.0000.0000.0000.0190.0000.0000.0000.0000.0110.0030.0000.0130.0000.0050.0000.0000.0050.0000.0060.0210.0030.0000.0000.0000.0250.0100.0000.0130.0170.0000.0050.0000.0000.0050.0000.0060.0000.0120.0000.0030.0000.0000.0000.0000.0080.0000.0120.0000.0000.0000.0470.0120.0000.0000.0000.0030.0000.0000.0000.0000.0170.0000.0110.0000.0000.0030.0130.0140.0000.0220.0130.0490.0330.0370.0340.0240.0180.0290.1610.1340.0690.0960.0550.0460.1680.0971.0000.0040.0140.0110.0060.0080.0110.0380.071
diag_3_Diabetes0.0520.0400.0400.0920.0690.0240.0670.0100.0000.0230.2740.0160.0190.0400.0120.0070.0100.0000.0000.0050.0040.0000.0120.0000.0020.0020.0000.0000.0220.0000.0000.0000.0000.0000.0090.0200.0180.0000.0130.0160.0050.0140.0160.0230.0340.0330.0090.0300.0520.0220.0000.0000.0050.0000.0000.0000.0000.0170.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0240.0080.0000.0300.0300.0000.0180.0000.0000.0110.0000.0000.0000.0280.0000.0000.0000.0000.0030.0000.0000.0000.0130.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0080.0220.0000.0300.0040.0030.0000.0000.0000.0060.0360.0150.0990.0130.0090.0190.0540.0070.0090.0210.0920.0090.0330.0270.0160.0000.0310.0041.0000.0960.1210.0700.0680.2450.1620.130
diag_3_Digestive0.0200.0200.0210.0080.0180.0530.0160.0000.0130.0140.0290.0090.0140.0160.0070.0060.0000.0130.0000.0170.0180.0000.0190.0120.0060.0000.0000.0000.0070.0000.0000.0000.0000.0280.0310.0000.0120.0110.0050.0110.0000.0000.0090.0190.0120.0020.0000.0080.0120.0100.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0120.0120.0000.0000.0090.0000.0000.0070.0100.0000.0000.0000.0000.0090.0000.0000.0020.0100.0000.0060.0000.0000.0000.0000.0000.0020.0030.0000.0000.0000.0000.0060.0000.0030.0080.0110.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0010.0090.0000.0100.0000.0080.0000.0000.0000.0020.0080.0000.0160.0020.0150.1570.0070.0270.0130.0120.0080.0270.0000.1320.0030.0180.0150.0110.0240.0140.0961.0000.0500.0290.0280.1020.0670.054
diag_3_Genitourinary0.0160.0160.0100.0570.0820.0250.0450.0180.0000.0190.1040.0080.0000.0490.0070.0080.0000.0000.0000.0000.0150.0040.0000.0000.0000.0000.0000.0000.0230.0090.0000.0000.0000.0030.0000.0240.0250.0020.0050.0020.0000.0130.0060.0130.0170.0220.0080.0140.0320.0270.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0020.0290.0000.0000.0000.0010.0060.0310.0070.0000.0070.0000.0290.0050.0000.0180.0170.0000.0080.0000.0000.0070.0000.0040.0000.0060.0000.0000.0000.0000.0000.0050.0000.0000.0120.0000.0000.0040.0120.0100.0000.0020.0000.0000.0030.0000.0100.0000.0090.0000.0090.0000.0000.0000.0000.0000.0000.0270.0570.0000.0110.0890.0000.0240.0260.0040.0240.0300.0160.0880.0090.0160.0000.0200.0110.1210.0501.0000.0360.0350.1280.0840.067
diag_3_Injury0.0530.0310.0110.0590.0110.0610.0640.0000.0240.0110.0530.0080.0120.0090.0030.0000.0000.0000.0000.0000.0090.0000.0070.0040.0000.0000.0000.0000.0110.0090.0030.0000.0000.0100.0030.0000.0000.0000.0070.0000.0060.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0120.0050.0000.0000.0020.0000.0000.0110.0000.0000.0000.0000.0000.0000.0030.0000.0110.0180.0000.0000.0000.0000.0080.0000.0000.0000.0040.0000.0000.0000.0000.0030.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0130.0000.0000.0000.0080.0000.0000.0020.0000.0080.0000.0180.0220.0160.1400.0020.0150.0110.0370.0280.0060.0000.1450.0000.0080.0040.0060.0700.0290.0361.0000.0200.0740.0490.039
diag_3_Muscoloskeletal0.0320.0250.0280.0140.0210.0190.0130.0050.0250.0000.0150.0000.0000.0220.0050.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0080.0080.0000.0000.0000.0000.0000.0010.0080.0000.0000.0000.0040.0090.0030.0000.0000.0090.0000.0000.0080.0000.0130.0000.0210.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0020.0000.0060.0210.0000.0000.0000.0080.0030.0000.0430.0440.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0020.0000.0000.0120.0000.0250.0000.0000.0000.0000.0080.0020.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0160.0000.0460.0220.0050.0000.1040.0070.0070.0130.0110.0090.0090.0000.1500.0000.0090.0080.0680.0280.0350.0201.0000.0720.0470.038
diag_3_Neoplasms0.0610.0360.0520.0320.0500.0850.0520.0070.0110.0040.0340.0110.0540.0070.0020.0000.0090.0000.0000.0100.0230.0030.0090.0000.0040.0000.0000.0030.0400.0000.0000.0000.0000.0230.0030.0020.0390.0000.0080.0000.0000.0410.0260.0390.0540.0100.0260.0390.0200.0000.0000.0120.0490.0060.0000.0000.0000.0330.0050.0000.0170.0030.0000.0080.0090.0000.0000.0190.0160.0060.0000.0000.0000.0250.0250.0040.0230.0200.0000.0030.0000.0000.0050.0000.0230.0000.0190.0000.0000.0000.0000.0070.0000.0100.0000.0390.0000.0000.0090.0000.0160.0000.0000.0000.0000.0000.0030.0240.0000.0100.0000.0180.0000.0030.0000.0040.0010.0000.0070.0200.1440.0010.0360.0340.0330.0620.0130.0050.0310.0000.0240.0380.0070.1230.0250.0110.2450.1020.1280.0740.0721.0000.1710.137
diag_3_Others0.0220.0240.0250.0320.0390.0450.0330.0120.0000.0030.0360.0020.0230.0060.0000.0000.0000.0070.0000.0130.0040.0000.0000.0020.0040.0000.0000.0000.0030.0000.0000.0000.0000.0000.0060.0050.0020.0000.0000.0030.0000.0250.0580.0370.0000.0090.0130.0080.0000.0080.0000.0000.0200.0000.0000.0000.0030.0120.0000.0000.0130.0030.0000.0000.0000.0000.0090.0150.0020.0080.0000.0000.0080.0780.0050.0140.0070.0160.0000.0030.0000.0000.0000.0000.0100.0000.0000.0000.0090.0000.0030.0300.0000.0020.0000.0020.0000.0000.0000.0000.0020.0020.0000.0000.0000.0100.0050.0000.0000.0040.0000.0000.0000.0000.0000.0040.0000.0190.0020.0050.0200.0000.0200.0900.0000.0090.0900.0380.0440.0240.0090.0460.0000.0150.0700.0380.1620.0670.0840.0490.0470.1711.0000.090
diag_3_Respiratory0.0250.0210.0200.0680.0490.0100.0810.0000.0000.0170.0960.0080.0050.0060.0000.0000.0000.0000.0000.0080.0000.0080.0000.0000.0000.0000.0010.0000.0210.0060.0000.0000.0000.0130.0090.0170.0170.0000.0220.0070.0000.0020.0140.0190.0170.0090.0080.0100.0130.0070.0000.0000.0040.0000.0000.0000.0000.0090.0070.0000.0070.0000.0050.0000.0000.0040.0000.0000.0050.0070.0000.0000.0000.0190.0000.0030.0170.0160.0060.0050.0000.0000.0000.0000.0000.0000.0070.0000.0000.0120.0000.0110.0000.0000.0000.0000.0000.0000.0020.0210.0000.0000.0000.0000.0000.0000.0000.0090.0000.0070.0000.0080.0000.0000.0050.0050.0040.0000.0100.0090.0240.0110.0160.0180.0290.0030.0190.0630.0390.0160.0080.0040.0180.0040.0260.0710.1300.0540.0670.0390.0380.1370.0901.000

Missing values

2023-06-04T23:07:03.709766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-04T23:07:05.538059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

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admission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideinsulinglyburide-metforminglipizide-metforminmetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmittednumchangerace_Asianrace_Caucasianrace_Hispanicrace_Otherage_[10-20)age_[20-30)age_[30-40)age_[40-50)age_[50-60)age_[60-70)age_[70-80)age_[80-90)age_[90-100)medical_specialty_Anesthesiologymedical_specialty_Anesthesiology-Pediatricmedical_specialty_Cardiologymedical_specialty_Cardiology-Pediatricmedical_specialty_DCPTEAMmedical_specialty_Dentistrymedical_specialty_Dermatologymedical_specialty_Emergency/Traumamedical_specialty_Endocrinologymedical_specialty_Endocrinology-Metabolismmedical_specialty_Family/GeneralPracticemedical_specialty_Gastroenterologymedical_specialty_Gynecologymedical_specialty_Hematologymedical_specialty_Hematology/Oncologymedical_specialty_Hospitalistmedical_specialty_InfectiousDiseasesmedical_specialty_InternalMedicinemedical_specialty_Nephrologymedical_specialty_Neurologymedical_specialty_Neurophysiologymedical_specialty_Obsterics&Gynecology-GynecologicOncomedical_specialty_Obstetricsmedical_specialty_ObstetricsandGynecologymedical_specialty_Oncologymedical_specialty_Ophthalmologymedical_specialty_Orthopedicsmedical_specialty_Orthopedics-Reconstructivemedical_specialty_Osteopathmedical_specialty_Otolaryngologymedical_specialty_OutreachServicesmedical_specialty_Pathologymedical_specialty_Pediatricsmedical_specialty_Pediatrics-AllergyandImmunologymedical_specialty_Pediatrics-CriticalCaremedical_specialty_Pediatrics-EmergencyMedicinemedical_specialty_Pediatrics-Endocrinologymedical_specialty_Pediatrics-Hematology-Oncologymedical_specialty_Pediatrics-Neurologymedical_specialty_Pediatrics-Pulmonologymedical_specialty_Perinatologymedical_specialty_PhysicalMedicineandRehabilitationmedical_specialty_PhysicianNotFoundmedical_specialty_Podiatrymedical_specialty_Proctologymedical_specialty_Psychiatrymedical_specialty_Psychiatry-Addictivemedical_specialty_Psychiatry-Child/Adolescentmedical_specialty_Psychologymedical_specialty_Pulmonologymedical_specialty_Radiologistmedical_specialty_Radiologymedical_specialty_Residentmedical_specialty_Rheumatologymedical_specialty_Speechmedical_specialty_Surgeonmedical_specialty_Surgery-Cardiovascularmedical_specialty_Surgery-Cardiovascular/Thoracicmedical_specialty_Surgery-Colon&Rectalmedical_specialty_Surgery-Generalmedical_specialty_Surgery-Maxillofacialmedical_specialty_Surgery-Neuromedical_specialty_Surgery-Pediatricmedical_specialty_Surgery-Plasticmedical_specialty_Surgery-PlasticwithinHeadandNeckmedical_specialty_Surgery-Thoracicmedical_specialty_Surgery-Vascularmedical_specialty_SurgicalSpecialtymedical_specialty_Unknowmedical_specialty_Urologydiag_1_Diabetesdiag_1_Digestivediag_1_Genitourinarydiag_1_Injurydiag_1_Muscoloskeletaldiag_1_Neoplasmsdiag_1_Othersdiag_1_Respiratorydiag_2_Diabetesdiag_2_Digestivediag_2_Genitourinarydiag_2_Injurydiag_2_Muscoloskeletaldiag_2_Neoplasmsdiag_2_Othersdiag_2_Respiratorydiag_3_Diabetesdiag_3_Digestivediag_3_Genitourinarydiag_3_Injurydiag_3_Muscoloskeletaldiag_3_Neoplasmsdiag_3_Othersdiag_3_Respiratory
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